Method and system for microbiome-derived characterization, diagnostics and therapeutics for conditions associated with functional features

ABSTRACT

A method for at least one of characterizing, diagnosing, and treating a condition associated with microbiome-derived functional features in at least a subject, the method comprising: receiving an aggregate set of biological samples from a population of subjects; generating at least one of a microbiome composition dataset and a microbiome functional diversity dataset for the population of subjects; generating a characterization of the condition based upon features extracted from at least one of the microbiome composition dataset and the microbiome functional diversity dataset; based upon the characterization, generating a therapy model configured to correct the condition; and at an output device associated with the subject, promoting a therapy to the subject based upon the characterization and the therapy model.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.15/098,110, filed 13 Apr. 2016, which is a continuation-in-part of U.S.application Ser. No. 14/919,614 filed 21 Oct. 2015, which claims thebenefit of U.S. Provisional Application Ser. No. 62/066,369 filed 21Oct. 2014, U.S. Provisional Application Ser. No. 62/087,551 filed 4 Dec.2014, U.S. Provisional Application Ser. No. 62/092,999 filed 17 Dec.2014, U.S. Provisional Application Ser. No. 62/147,376 filed 14 Apr.2015, U.S. Provisional Application Ser. No. 62/147,212 filed 14 Apr.2015, U.S. Provisional Application Ser. No. 62/147,362 filed 14 Apr.2015, U.S. Provisional Application Ser. No. 62/146,855 filed 13 Apr.2015, and U.S. Provisional Application Ser. No. 62/206,654 filed 18 Aug.2015, which are each incorporated in its entirety herein by thisreference.

This application is a continuation of U.S. application Ser. No.15/098,110, which claims the benefit of U.S. Provisional ApplicationSer. No. 62/146,818 filed 13 Apr. 2015, U.S. Provisional ApplicationSer. No. 62/147,096 filed 14 Apr. 2015, and U.S. Provisional ApplicationSer. No. 62/147,334 filed 14 Apr. 2015, which are each incorporated inits entirety herein by this reference.

TECHNICAL FIELD

This invention relates generally to the field of medical diagnostics andmore specifically to a new and useful method and system formicrobiome-derived diagnostics and therapeutics in the field of medicaldiagnostics.

BACKGROUND

A microbiome is an ecological community of commensal, symbiotic, andpathogenic microorganisms that are associated with an organism. Thehuman microbiome comprises as many microbial cells as human cellspresent in the entire body, but characterization of the human microbiomeis still in nascent stages due to limitations in sample processingtechniques, genetic analysis techniques, and resources for processinglarge amounts of data. Nonetheless, the microbiome is suspected to playat least a partial role in a number of health/disease-related states(e.g., preparation for childbirth, gastrointestinal disorders, etc.).

Given the profound implications of the microbiome in affecting asubject's health, efforts related to the characterization of themicrobiome, the generation of insights from the characterization, andthe generation of therapeutics configured to rectify states of dysbiosisshould be pursued. Current methods and systems for analyzing themicrobiomes of humans and providing therapeutic measures based on gainedinsights have, however, left many questions unanswered. In particular,methods for characterizing certain health conditions and therapies(e.g., probiotic therapies) tailored to specific subjects have not beenviable due to limitations in current technologies.

As such, there is a need in the field of microbiology for a new anduseful method and system for characterizing conditions associated withmicrobiome taxonomic groups in an individualized and population-widemanner. This invention creates such a new and useful method and system.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1A is a flowchart of an embodiment of a method for characterizing amicrobiome-derived condition and identifying therapeutic measures;

FIG. 1B is a flowchart of an embodiment of a method for generatingmicrobiome-derived diagnostics;

FIG. 1C is a flowchart of a portion of a variation of a method forgenerating microbiome-derived diagnostics and therapeutics;

FIG. 2 depicts an embodiment of a method and system for generatingmicrobiome-derived diagnostics and therapeutics;

FIG. 3 depicts variations of a portion of an embodiment of a method forgenerating microbiome-derived diagnostics and therapeutics;

FIG. 4 depicts a variation of a process for generation of a model in anembodiment of a method and system for generating microbiome-deriveddiagnostics and therapeutics;

FIG. 5 depicts variations of mechanisms by which probiotic-basedtherapies operate in an embodiment of a method for characterizing ahealth condition; and

FIG. 6 depicts examples of therapy-related notification provision in anexample of a method for generating microbiome-derived diagnostics andtherapeutics.

DESCRIPTION OF THE EMBODIMENTS

The following description of the embodiments of the invention is notintended to limit the invention to these embodiments, but rather toenable any person skilled in the art to make and use this invention.

1. Method for Characterizing a Microbiome-Derived Condition andIdentifying Therapeutic Measures

As shown in FIG. 1A, a first method 100 for diagnosing and treating acondition associated with microbiome functional features comprises:receiving an aggregate set of samples from a population of subjectsS110; characterizing a microbiome composition and/or functional featuresfor each of the aggregate set of biological samples associated with thepopulation of subjects, thereby generating at least one of a microbiomecomposition dataset and a microbiome functional diversity dataset forthe population of subjects S120; receiving a supplementary dataset,associated with at least a subset of the population of subjects, whereinthe supplementary dataset is informative of characteristics associatedwith the condition S130; and transforming the supplementary dataset andfeatures extracted from at least one of the microbiome compositiondataset and the microbiome functional diversity dataset into acharacterization model of the condition S140. In some variations, thefirst method 100 can further include: based upon the characterization,generating a therapy model configured to improve a state of conditionS150.

Variations of the first method 100 can additionally or alternativelycomprise clustering otherwise unrelated conditions based upon featuresextracted from at least one of a microbiome functional diversity datasetand/or a microbiome composition dataset by way of a characterizationmodel S140′; and diagnosing a subject with a condition based uponidentification of microbiome functional features and/or microbiomecomposition features, determined upon processing a sample from thesubject, in a non-obvious manner S141′.

The first method 100 functions to generate models that can be used tocharacterize and/or diagnose subjects according to at least one of theirmicrobiome composition and functional features (e.g., as a clinicaldiagnostic, as a companion diagnostic, etc.), and provide therapeuticmeasures (e.g., probiotic-based therapeutic measures, phage-basedtherapeutic measures, small-molecule-based therapeutic measures,prebiotic-based therapeutic measures, clinical measures, etc.) tosubjects based upon microbiome analysis for a population of subjects. Assuch, data from the population of subjects can be used to characterizesubjects according to their microbiome composition and/or functionalfeatures, indicate states of health and areas of improvement based uponthe characterization(s), and promote one or more therapies that canmodulate the composition of a subject's microbiome toward one or more ofa set of desired equilibrium states.

In variations, the method 100 can be used to promote targeted therapiesto subjects suffering from a condition associated with microbiomefunctional features (e.g., functional features associated with anaminobenzoate degradation KEGG L3 derived feature and/or a streptomycinbiosynthesis KEGG L3 derived feature), wherein the condition affects thesubject in some manner, and produces physiological and/or psychologicaleffects in relation to one or more of: energy levels, metabolism,physiologic function (e.g., gastrointestinal system function, endocrinesystem function, cardiovascular system function, neurological systemfunction, immune system function, musculoskeletal system function,etc.), pain, and any other suitable physiological or psychologicaleffect. In particular, the method 100 can be adapted forcharacterization and/or diagnostics associated with seemingly unrelatedconditions, wherein microbiome functional features are otherwise sharedacross the conditions in a non-obvious manner. As such, the method 100can be used to diagnose and/or treat conditions that are typicallyunassociated with each other, using microbiome-based diagnostics andmicrobiome modulating therapeutics in a novel manner.

In these variations, diagnostics associated with the condition aretypically assessed using one or more of: a blood cell analysis of abiological sample, endoscopy, an imaging based method, biopsy, abehavioral survey instrument (e.g., a Patient Health Questionnaire-9(PHQ-9) survey, a patient health questionnaire-2 (PHQ-2) survey, aninstrument derived from an edition of the Diagnostic and StatisticalManual (DSM) of mental disorders, an instrument derived from the SocialCommunication Questionnaire (SCQ), a Clinical Global Impression (CGI)scale, a Brief Psychiatric Rating Scale, etc.); a motor skills basedassessment, and any other standard method. In specific examples, themethod 100 can be used for characterization of and/or therapeuticintervention for one or more of: AIDS, Sprue, gastric cancer, and anyother associated condition. As such, the method 100 can be used tocharacterize such seemingly unrelated conditions, disorders, and/oradverse states in an entirely non-typical method, using microbiome-baseddiagnostics and/or therapeutics.

As such, in some embodiments, outputs of the first method 100 can beused to generate diagnostics and/or provide therapeutic measures for asubject based upon an analysis of the subject's microbiome compositionand/or functional features of the subject's microbiome. Thus, as shownin FIG. 1B, a second method 200 derived from at least one output of thefirst method 100 can include: receiving a biological sample from asubject S210; characterizing the subject with a form of a conditionassociated with microbiome functional features based upon processing amicrobiome dataset derived from the biological sample S220; andpromoting a therapy to the subject with the condition based upon thecharacterization and the therapy model S230. Variations of the method100 can further facilitate monitoring and/or adjusting of therapiesprovided to a subject, for instance, through reception, processing, andanalysis of additional samples from a subject throughout the course oftherapy. Embodiments, variations, and examples of the second method 200are described in more detail below.

The methods thus 100, 200 function to generate models that can be usedto classify individuals and/or provide therapeutic measures (e.g.,therapy recommendations, therapies, therapy regimens, etc.) toindividuals based upon microbiome analysis for a population ofindividuals. As such, data from the population of individuals can beused to generate models that can classify individuals according to theirmicrobiome compositions (e.g., as a diagnostic measure), indicate statesof health and areas of improvement based upon the classification(s),and/or provide therapeutic measures that can push the composition of anindividual's microbiome toward one or more of a set of improvedequilibrium states. Variations of the second method 200 can furtherfacilitate monitoring and/or adjusting of therapies provided to anindividual, for instance, through reception, processing, and analysis ofadditional samples from an individual throughout the course of therapy.

In one application, at least one of the methods 100, 200 is implemented,at least in part, at a system 300, as shown in FIG. 2, that receives abiological sample derived from the subject (or an environment associatedwith the subject) by way of a sample reception kit, and processes thebiological sample at a processing system implementing a characterizationprocess and a therapy model configured to positively influence amicroorganism distribution in the subject (e.g., human, non-humananimal, environmental ecosystem, etc.). In variations of theapplication, the processing system can be configured to generate and/orimprove the characterization process and the therapy model based uponsample data received from a population of subjects. The method 100 can,however, alternatively be implemented using any other suitable system(s)configured to receive and process microbiome-related data of subjects,in aggregation with other information, in order to generate models formicrobiome-derived diagnostics and associated therapeutics. Thus, themethod 100 can be implemented for a population of subjects (e.g.,including the subject, excluding the subject), wherein the population ofsubjects can include patients dissimilar to and/or similar to thesubject (e.g., in health condition, in dietary needs, in demographicfeatures, etc.). Thus, information derived from the population ofsubjects can be used to provide additional insight into connectionsbetween behaviors of a subject and effects on the subject's microbiome,due to aggregation of data from a population of subjects.

Thus, the methods 100, 200 can be implemented for a population ofsubjects (e.g., including the subject, excluding the subject), whereinthe population of subjects can include subjects dissimilar to and/orsimilar to the subject (e.g., health condition, in dietary needs, indemographic features, etc.). Thus, information derived from thepopulation of subjects can be used to provide additional insight intoconnections between behaviors of a subject and effects on the subject'smicrobiome, due to aggregation of data from a population of subjects.

1.1 First Method: Sample Handling

Block S110 recites: receiving an aggregate set of biological samplesfrom a population of subjects, which functions to enable generation ofdata from which models for characterizing subjects and/or providingtherapeutic measures to subjects can be generated. In Block S110,biological samples are preferably received from subjects of thepopulation of subjects in a non-invasive manner. In variations,non-invasive manners of sample reception can use any one or more of: apermeable substrate (e.g., a swab configured to wipe a region of asubject's body, toilet paper, a sponge, etc.), a non-permeable substrate(e.g., a slide, tape, etc.), a container (e.g., vial, tube, bag, etc.)configured to receive a sample from a region of a subject's body, andany other suitable sample-reception element. In a specific example,samples can be collected from one or more of a subject's nose, skin,genitals, mouth, and gut in a non-invasive manner (e.g., using a swaband a vial). However, one or more biological samples of the set ofbiological samples can additionally or alternatively be received in asemi-invasive manner or an invasive manner. In variations, invasivemanners of sample reception can use any one or more of: a needle, asyringe, a biopsy element, a lance, and any other suitable instrumentfor collection of a sample in a semi-invasive or invasive manner. Inspecific examples, samples can comprise blood samples, plasma/serumsamples (e.g., to enable extraction of cell-free DNA), and tissuesamples.

In the above variations and examples, samples can be taken from thebodies of subjects without facilitation by another entity (e.g., acaretaker associated with an individual, a health care professional, anautomated or semi-automated sample collection apparatus, etc.), or canalternatively be taken from bodies of individuals with the assistance ofanother entity. In one example, wherein samples are taken from thebodies of subjects without facilitation by another entity in the sampleextraction process, a sample-provision kit can be provided to a subject.In the example, the kit can include one or more swabs for sampleacquisition, one or more containers configured to receive the swab(s)for storage, instructions for sample provision and setup of a useraccount, elements configured to associate the sample(s) with the subject(e.g., barcode identifiers, tags, etc.), and a receptacle that allowsthe sample(s) from the individual to be delivered to a sample processingoperation (e.g., by a mail delivery system). In another example, whereinsamples are extracted from the user with the help of another entity, oneor more samples can be collected in a clinical or research setting froma subject (e.g., during a clinical appointment).

In Block S110, the aggregate set of biological samples is preferablyreceived from a wide variety of subjects, and can involve samples fromhuman subjects and/or non-human subjects. In relation to human subjects,Block S110 can include receiving samples from a wide variety of humansubjects, collectively including subjects of one or more of: differentdemographics (e.g., genders, ages, marital statuses, ethnicities,nationalities, socioeconomic statuses, sexual orientations, etc.),different health conditions (e.g., health and disease states), differentliving situations (e.g., living alone, living with pets, living with asignificant other, living with children, etc.), different dietary habits(e.g., omnivorous, vegetarian, vegan, sugar consumption, acidconsumption, etc.), different behavioral tendencies (e.g., levels ofphysical activity, drug use, alcohol use, etc.), different levels ofmobility (e.g., related to distance traveled within a given timeperiod), biomarker states (e.g., cholesterol levels, lipid levels,etc.), weight, height, body mass index, genotypic factors, and any othersuitable trait that has an effect on microbiome composition. As such, asthe number of subjects increases, the predictive power of feature-basedmodels generated in subsequent blocks of the method 100 increases, inrelation to characterizing a variety of subjects based upon theirmicrobiomes. Additionally or alternatively, the aggregate set ofbiological samples received in Block S110 can include receivingbiological samples from a targeted group of similar subjects in one ormore of: demographic traits, health conditions, living situations,dietary habits, behavior tendencies, levels of mobility, age range(e.g., pediatric, adulthood, geriatric), and any other suitable traitthat has an effect on microbiome composition. Additionally oralternatively, the methods 100, 200 can be adapted to characterizeconditions typically detected by way of lab tests (e.g., polymerasechain reaction based tests, cell culture based tests, blood tests,biopsies, chemical tests, etc.), physical detection methods (e.g.,manometry), medical history based assessments, behavioral assessments,and imagenology based assessments. Additionally or alternatively, themethods 100, 200 can be adapted to characterization of acute conditions,chronic conditions, conditions with difference in prevalence fordifferent demographics, conditions having characteristic disease areas(e.g., the head, the gut, endocrine system diseases, the heart, nervoussystem diseases, respiratory diseases, immune system diseases,circulatory system diseases, renal system diseases, locomotor systemdiseases, etc.), and comorbid conditions.

In some embodiments, receiving the aggregate set of biological samplesin Block S110 can be performed according to embodiments, variations, andexamples of sample reception as described in U.S. application Ser. No.14/593,424 filed on 9 Jan. 2015 and entitled “Method and System forMicrobiome Analysis”, which is incorporated herein in its entirety bythis reference. However, receiving the aggregate set of biologicalsamples in Block S110 can additionally or alternatively be performed inany other suitable manner. Furthermore, some variations of the firstmethod 100 can omit Block S110, with processing of data derived from aset of biological samples performed as described below in subsequentblocks of the method 100.

1.2 First Method: Sample Analysis, Microbiome Composition, andFunctional Aspects

Block S120 recites: characterizing a microbiome composition and/orfunctional features for each of the aggregate set of biological samplesassociated with a population of subjects, thereby generating at leastone of a microbiome composition dataset and a microbiome functionaldiversity dataset for the population of subjects. Block S120 functionsto process each of the aggregate set of biological samples, in order todetermine compositional and/or functional aspects associated with themicrobiome of each of a population of subjects. Compositional andfunctional aspects can include compositional aspects at themicroorganism level, including parameters related to distribution ofmicroorganisms across different groups of kingdoms, phyla, classes,orders, families, genera, species, subspecies, strains, infraspeciestaxon (e.g., as measured in total abundance of each group, relativeabundance of each group, total number of groups represented, etc.),and/or any other suitable taxa. Compositional and functional aspects canalso be represented in terms of operational taxonomic units (OTUs).Compositional and functional aspects can additionally or alternativelyinclude compositional aspects at the genetic level (e.g., regionsdetermined by multilocus sequence typing, 16S sequences, 18S sequences,ITS sequences, other genetic markers, other phylogenetic markers, etc.).Compositional and functional aspects can include the presence or absenceor the quantity of genes associated with specific functions (e.g.,enzyme activities, transport functions, immune activities, etc.).Outputs of Block S120 can thus be used to provide features of interestfor the characterization process of Block S140,wherein the features canbe microorganism-based (e.g., presence of a genus of bacteria),genetic-based (e.g., based upon representation of specific geneticregions and/or sequences) and/or functional-based (e.g., presence of aspecific catalytic activity, presence of metabolic pathways, etc.).

In one variation, Block S120 can include characterization of featuresbased upon identification of phylogenetic markers derived from bacteriaand/or archaea in relation to gene families associated with one or moreof: ribosomal protein S2, ribosomal protein S3, ribosomal protein S5,ribosomal protein S7, ribosomal protein S8, ribosomal protein S9,ribosomal protein S10, ribosomal protein S11, ribosomal protein S12/S23,ribosomal protein S13, ribosomal protein S15P/S13e, ribosomal proteinS17, ribosomal protein S19, ribosomal protein L1, ribosomal protein L2,ribosomal protein L3, ribosomal protein L4/L1e, ribosomal protein L5,ribosomal protein L6, ribosomal protein L10, ribosomal protein L11,ribosomal protein L13, ribosomal protein L14b/L23e, ribosomal proteinL15, ribosomal protein L16/L10E, ribosomal protein L18P/L5E, ribosomalprotein L22, ribosomal protein L24, ribosomal protein L25/L23, ribosomalprotein L29, translation elongation factor EF-2, translation initiationfactor IF-2, metalloendopeptidase, ffh signal recognition particleprotein, phenylalanyl-tRNA synthetase alpha subunit, phenylalanyl-tRNAsynthetase beta subunit, tRNA pseudouridine synthase B, porphobilinogendeaminase, phosphoribosylformylglycinamidine cyclo-ligase, andribonuclease HII. However, the markers can include any other suitablemarker(s)

Characterizing the microbiome composition and/or functional features foreach of the aggregate set of biological samples in Block S120 thuspreferably includes a combination of sample processing techniques (e.g.,wet laboratory techniques) and computational techniques (e.g., utilizingtools of bioinformatics) to quantitatively and/or qualitativelycharacterize the microbiome and functional features associated with eachbiological sample from a subject or population of subjects.

In variations, sample processing in Block S120 can include any one ormore of: lysing a biological sample, disrupting membranes in cells of abiological sample, separation of undesired elements (e.g., RNA,proteins) from the biological sample, purification of nucleic acids(e.g., DNA) in a biological sample, amplification of nucleic acids fromthe biological sample, further purification of amplified nucleic acidsof the biological sample, and sequencing of amplified nucleic acids ofthe biological sample. Thus, portions of Block S120 can be implementedusing embodiments, variations, and examples of the sample handlingnetwork and/or computing system as described in U.S. application Ser.No. 14/593,424 filed on 9 Jan. 2015 and entitled “Method and System forMicrobiome Analysis”, which is incorporated herein in its entirety bythis reference. Thus the computing system implementing one or moreportions of the method 100 can be implemented in one or more computingsystems, wherein the computing system(s) can be implemented at least inpart in the cloud and/or as a machine (e.g., computing machine, server,mobile computing device, etc.) configured to receive a computer-readablemedium storing computer-readable instructions. However, Block S120 canbe performed using any other suitable system(s).

In variations, lysing a biological sample and/or disrupting membranes incells of a biological sample preferably includes physical methods (e.g.,bead beating, nitrogen decompression, homogenization, sonication), whichomit certain reagents that produce bias in representation of certainbacterial groups upon sequencing. Additionally or alternatively, lysingor disrupting in Block S120 can involve chemical methods (e.g., using adetergent, using a solvent, using a surfactant, etc.). Additionally oralternatively, lysing or disrupting in Block S120 can involve biologicalmethods. In variations, separation of undesired elements can includeremoval of RNA using RNases and/or removal of proteins using proteases.In variations, purification of nucleic acids can include one or more of:precipitation of nucleic acids from the biological samples (e.g., usingalcohol-based precipitation methods), liquid-liquid based purificationtechniques (e.g., phenol-chloroform extraction), chromatography-basedpurification techniques (e.g., column adsorption), purificationtechniques involving use of binding moiety-bound particles (e.g.,magnetic beads, buoyant beads, beads with size distributions,ultrasonically responsive beads, etc.) configured to bind nucleic acidsand configured to release nucleic acids in the presence of an elutionenvironment (e.g., having an elution solution, providing a pH shift,providing a temperature shift, etc.), and any other suitablepurification techniques.

In variations, performing an amplification operation S123 on purifiednucleic acids can include performing one or more of: polymerase chainreaction (PCR)-based techniques (e.g., solid-phase PCR, RT-PCR, qPCR,multiplex PCR, touchdown PCR, nanoPCR, nested PCR, hot start PCR, etc.),helicase-dependent amplification (HDA), loop mediated isothermalamplification (LAMP), self-sustained sequence replication (3SR), nucleicacid sequence based amplification (NASBA), strand displacementamplification (SDA), rolling circle amplification (RCA), ligase chainreaction (LCR), and any other suitable amplification technique. Inamplification of purified nucleic acids, the primers used are preferablyselected to prevent or minimize amplification bias, as well asconfigured to amplify nucleic acid regions/sequences (e.g., of the 16Sregion, the 18S region, the ITS region, etc.) that are informativetaxonomically, phylogenetically, for diagnostics, for formulations(e.g., for probiotic formulations), and/or for any other suitablepurpose. Thus, universal primers (e.g., a F27-R338 primer set for 16SRNA, a F515-R806 primer set for 16S RNA, etc.) configured to avoidamplification bias can be used in amplification. Primers used invariations of Block S110 can additionally or alternatively includeincorporated barcode sequences specific to each biological sample, whichcan facilitate identification of biological samples post-amplification.Primers used in variations of Block S110 can additionally oralternatively include adaptor regions configured to cooperate withsequencing techniques involving complementary adaptors (e.g., accordingto protocols for Illumina Sequencing).

Identification of a primer set for a multiplexed amplification operationcan be performed according to embodiments, variations, and examples ofmethods described in U.S. application Ser. No. 62/206,654 filed 18 Aug.2015 and entitled “Method and System for Multiplex Primer Design”, whichis herein incorporated in its entirety by this reference. Performing amultiplexed amplification operation using a set of primers in Block S123can additionally or alternatively be performed in any other suitablemanner.

Additionally or alternatively, as shown in FIG. 3, Block S120 canimplement any other step configured to facilitate processing (e.g.,using a Nextera kit) for performance of a fragmentation operation S122(e.g., fragmentation and tagging with sequencing adaptors) incooperation with the amplification operation S123 (e.g., S122 can beperformed after S123, S122 can be performed before S123, S122 can beperformed substantially contemporaneously with S123, etc.) Furthermore,Blocks S122 and/or S123 can be performed with or without a nucleic acidextraction step. For instance, extraction can be performed prior toamplification of nucleic acids, followed by fragmentation, and thenamplification of fragments. Alternatively, extraction can be performed,followed by fragmentation and then amplification of fragments. As such,in some embodiments, performing an amplification operation in Block S123can be performed according to embodiments, variations, and examples ofamplification as described in U.S. application Ser. No. 14/593,424 filedon 9 Jan. 2015 and entitled “Method and System for Microbiome Analysis”.Furthermore, amplification in Block S123 can additionally oralternatively be performed in any other suitable manner.

In a specific example, amplification and sequencing of nucleic acidsfrom biological samples of the set of biological samples includes:solid-phase PCR involving bridge amplification of DNA fragments of thebiological samples on a substrate with oligo adapters, whereinamplification involves primers having a forward index sequence (e.g.,corresponding to an Illumina forward index for MiSeq/NextSeq/HiSeqplatforms) or a reverse index sequence (e.g., corresponding to anIllumina reverse index for MiSeq/NextSeq/HiSeq platforms), a forwardbarcode sequence or a reverse barcode sequence, a transposase sequence(e.g., corresponding to a transposase binding site forMiSeq/NextSeq/HiSeq platforms), a linker (e.g., a zero, one, or two-basefragment configured to reduce homogeneity and improve sequence results),an additional random base, and a sequence for targeting a specifictarget region (e.g., 16S region, 18S region, ITS region). Amplificationand sequencing can further be performed on any suitable amplicon, asindicated throughout the disclosure. In the specific example, sequencingcomprises Illumina sequencing (e.g., with a HiSeq platform, with a MiSeqplatform, with a NextSeq platform, etc.) using a sequencing-by-synthesistechnique. Additionally or alternatively, any other suitable nextgeneration sequencing technology (e.g., PacBio platform, MinIONplatform, Oxford Nanopore platform, etc.) can be used. Additionally oralternatively, any other suitable sequencing platform or method can beused (e.g., a Roche 454 Life Sciences platform, a Life TechnologiesSOLiD platform, etc.). In examples, sequencing can include deepsequencing to quantify the number of copies of a particular sequence ina sample and then also be used to determine the relative abundance ofdifferent sequences in a sample. Deep sequencing refers to highlyredundant sequencing of a nucleic acid sequence, for example such thatthe original number of copies of a sequence in a sample can bedetermined or estimated. The redundancy (i.e., depth) of the sequencingis determined by the length of the sequence to be determined (X), thenumber of sequencing reads (N), and the average read length (L). Theredundancy is then NxL/X. The sequencing depth can be, or be at leastabout 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37,38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55,56, 57, 58, 59, 60, 70, 80, 90, 100, 110, 120, 130, 150, 200, 300, 500,500, 700, 1000, 2000, 3000, 4000, 5000 or more.

Some variations of sample processing in Block S120 can include furtherpurification of amplified nucleic acids (e.g., PCR products) prior tosequencing, which functions to remove excess amplification elements(e.g., primers, dNTPs, enzymes, salts, etc.). In examples, additionalpurification can be facilitated using any one or more of: purificationkits, buffers, alcohols, pH indicators, chaotropic salts, nucleic acidbinding filters, centrifugation, and any other suitable purificationtechnique.

In variations, computational processing in Block S120 can include anyone or more of: performing a sequencing analysis operation S124including identification of microbiome-derived sequences (e.g., asopposed to subject sequences and contaminants), performing an alignmentand/or mapping operation S125 of microbiome-derived sequences (e.g.,alignment of fragmented sequences using one or more of single-endedalignment, ungapped alignment, gapped alignment, pairing), andgenerating features S126 derived from compositional and/or functionalaspects of the microbiome associated with a biological sample.

Performing the sequencing analysis operation S124 with identification ofmicrobiome-derived sequences can include mapping of sequence data fromsample processing to a subject reference genome (e.g., provided by theGenome Reference Consortium), in order to remove subject genome-derivedsequences. Unidentified sequences remaining after mapping of sequencedata to the subject reference genome can then be further clustered intooperational taxonomic units (OTUs) based upon sequence similarity and/orreference-based approaches (e.g., using VAMPS, using MG-RAST, usingQIIME databases), aligned (e.g., using a genome hashing approach, usinga Needleman-Wunsch algorithm, using a Smith-Waterman algorithm), andmapped to reference bacterial genomes (e.g., provided by the NationalCenter for Biotechnology Information), using an alignment algorithm(e.g., Basic Local Alignment Search Tool, FPGA accelerated alignmenttool, BWT-indexing with BWA, BWT-indexing with SOAP, BWT-indexing withBowtie, etc.). Mapping of unidentified sequences can additionally oralternatively include mapping to reference archaeal genomes, viralgenomes and/or eukaryotic genomes. Furthermore, mapping of taxa can beperformed in relation to existing databases, and/or in relation tocustom-generated databases.

Additionally or alternatively, in relation to generating a microbiomefunctional diversity dataset, Block S120 can include extractingcandidate features associated with functional aspects of one or moremicrobiome components of the aggregate set of biological samples S127,as indicated in the microbiome composition dataset. Extracting candidatefunctional features can include identifying functional featuresassociated with one or more of: prokaryotic clusters of orthologousgroups of proteins (COGs); eukaryotic clusters of orthologous groups ofproteins (KOGs); any other suitable type of gene product; an RNAprocessing and modification functional classification; a chromatinstructure and dynamics functional classification; an energy productionand conversion functional classification; a cell cycle control andmitosis functional classification; an amino acid metabolism andtransport functional classification; a nucleotide metabolism andtransport functional classification; a carbohydrate metabolism andtransport functional classification; a coenzyme metabolism functionalclassification; a lipid metabolism functional classification; atranslation functional classification; a transcription functionalclassification; a replication and repair functional classification; acell wall/membrane/envelop biogenesis functional classification; a cellmotility functional classification; a post-translational modification,protein turnover, and chaperone functions functional classification; aninorganic ion transport and metabolism functional classification; asecondary metabolites biosynthesis, transport and catabolism functionalclassification; a signal transduction functional classification; anintracellular trafficking and secretion functional classification; anuclear structure functional classification; a cytoskeleton functionalclassification; a general functional prediction only functionalclassification; and a function unknown functional classification; andany other suitable functional classification.

Additionally or alternatively, extracting candidate functional featuresin Block S127 can include identifying functional features associatedwith one or more of: systems information (e.g., pathway maps forcellular and organismal functions, modules or functional units of genes,hierarchical classifications of biological entities); genomicinformation (e.g., complete genomes, genes and proteins in the completegenomes, orthologous groups of genes in the complete genomes); chemicalinformation (e.g., chemical compounds and glycans, chemical reactions,enzyme nomenclature); health information (e.g., human diseases, approveddrugs, crude drugs and health-related substances); metabolism pathwaymaps; genetic information processing (e.g., transcription, translation,replication and repair, etc.) pathway maps; environmental informationprocessing (e.g., membrane transport, signal transduction, etc.) pathwaymaps; cellular processes (e.g., cell growth, cell death, cell membranefunctions, etc.) pathway maps; organismal systems (e.g., immune system,endocrine system, nervous system, etc.) pathway maps; human diseasepathway maps; drug development pathway maps; and any other suitablepathway map.

In extracting candidate functional features, Block S127 can compriseperforming a search of one or more databases, such as the KyotoEncyclopedia of Genes and Genomes (KEGG) and/or the Clusters ofOrthologous Groups (COGs) database managed by the National Center forBiotechnology Information (NCBI). Searching can be performed based uponresults of generation of the microbiome composition dataset from one ormore of the set of aggregate biological samples and/or sequencing ofmaterial from the set of samples. In more detail, Block S127 can includeimplementation of a data-oriented entry point to a KEGG databaseincluding one or more of a KEGG pathway tool, a KEGG BRITE tool, a KEGGmodule tool, a KEGG ORTHOLOGY (KO) tool, a KEGG genome tool, a KEGGgenes tool, a KEGG compound tool, a KEGG glycan tool, a KEGG reactiontool, a KEGG disease tool, a KEGG drug tool, a KEGG medicus tool,Searching can additionally or alternatively be performed according toany other suitable filters. Additionally or alternatively, Block S127can include implementation of an organism-specific entry point to a KEGGdatabase including a KEGG organisms tool. Additionally or alternatively,Block S127 can include implementation of an analysis tool including oneor more of: a KEGG mapper tool that maps KEGG pathway, BRITE, or moduledata; a KEGG atlas tool for exploring KEGG global maps, a BlastKOALAtool for genome annotation and KEGG mapping, a BLAST/FASTA sequencesimilarity search tool, and a SIMCOMP chemical structure similaritysearch tool. In specific examples, Block S127 can include extractingcandidate functional features, based on the microbiome compositiondataset, from a KEGG database resource and a COG database resource;however, Block S127 can comprise extracting candidate functionalfeatures in any other suitable manner. For instance, Block S127 caninclude extracting candidate functional features, including functionalfeatures derived from a Gene Ontology functional classification, and/orany other suitable features.

In one example, a taxonomic group can include one or more bacteria andtheir corresponding reference sequences. A sequence read can be assignedbased on the alignment to a taxonomic group when the sequence readaligns to a reference sequence of the taxonomic group. A functionalgroup can correspond to one or more genes labeled as having a similarfunction. Thus, a functional group can be represented by referencesequences of the genes in the functional group, where the referencesequences of a particular gene can correspond to various bacteria. Thetaxonomic and functional groups can collectively be referred to assequence groups, as each group includes one or more reference sequencesthat represent the group. A taxonomic group of multiple bacteria can berepresented by multiple reference sequence, e.g., one reference sequenceper bacteria species in the taxonomic group. Embodiments can use thedegree of alignment of a sequence read to multiple reference sequencesto determine which sequence group to assign the sequence read based onthe alignment.

1.2.1 Examples and Variations: Sequence Group Corresponds to TaxonomicGroup

A taxonomic group can correspond to any set of one or more referencesequences for one or more loci (e.g., genes) that represent thetaxonomic group. Any given level of a taxonomic hierarchy would includea plurality of taxonomic groups. For instance, a reference sequence inthe one group at the genus level can be in another group at the familylevel.

The RAV can correspond to the proportion of reads assigned to aparticular taxonomic group. The proportion can be relative to variousdenominator values, e.g., relative to all of the sequence reads,relative to all assigned to at least one group (taxonomic orfunctional), or all assigned to for a given level in the hierarchy. Thealignment can be implemented in any manner that can assign a sequenceread to a particular taxonomic group.

For example, based on the mappings to the reference sequence(s) in the16S region, a taxonomic group with the best match for the alignment canbe identified. The RAV can then be determined for that taxonomic groupusing the number of sequence reads (or votes of sequence reads) for aparticular sequence group divided by the number of sequence readsidentified as being bacterial, which may be for a specific region oreven for a given level of a hierarchy.

1.2.2 Examples and Variations: Sequence Group Corresponds to FunctionalGroup or Gene

Instead of or in addition to determining a count of the sequence readsthat correspond to a particular taxonomic group, embodiments can use acount of a number of sequence reads that correspond to a particular geneor a collection of genes having an annotation of a particular function,where the collection is called a functional group. The RAV can bedetermined in a similar manner as for a taxonomic group. For example,functional group can include a plurality of reference sequencescorresponding to one or more genes of the functional group. Referencesequences of multiple bacteria for a same gene can correspond to a samefunctional group. Then, to determine the RAV, the number of sequencereads assigned to the functional group can be used to determine aproportion for the functional group.

The use of a function group, which may include a single gene, can helpto identify situations where there is a small change (e.g., increase) inmany taxonomic groups such that the change is too small to bestatistically significant. But, the changes may all be for a same geneor set of genes of a same functional group, and thus the change for thatfunctional group can be statistically significant, even though thechanges for the taxonomic groups may not be significant. The reverse canbe true of a taxonomic group being more predictive than a particularfunctional group, e.g., when a single taxonomic group includes manygenes that have change by a relatively small amount.

As an example, if 10 taxonomic groups increase by 10%, the statisticalpower to discriminate between the two groups may be low when eachtaxonomic group is analyzed individually. But, if the increase is allfor genes(s) of a same functional group, then the increase would be100%, or a doubling of the proportion for that taxonomic group. Thislarge increase would have a much larger statistical power fordiscriminating between the two groups. Thus, the functional group canact to provide a sum of small changes for various taxonomic groups. And,small changes for various functional groups, which happen to all be on asame taxonomic group, can sum to provide high statistical power for thatparticular taxonomic group.

The taxonomic groups and functional groups can supplement each other asthe information can be orthogonal, or at least partially orthogonal asthere still may be some relationship between the RAVs of each group. Forexample, the RAVs of one or more taxonomic groups and functional groupscan be used together as multiple features of a feature vector, which isanalyzed to provide a diagnosis, as is described herein. For instance,the feature vector can be compared to a disease signature as part of acharacterization model.

1.2.3 Examples and Variations: Pipeline for Taxonomic Groups

Embodiments can provide a bioinformatics pipeline that taxonomicallyannotates the microorganisms present in a sample. The example annotationpipeline can comprise the following procedures.

In a first block, the samples can be identified and the sequence datacan be loaded. For example, the pipeline can begin with demultiplexedfastq files (or other suitable files) that are the product of pair-endsequencing of amplicons (e.g., of the V4 region of the 16S gene). Allsamples can be identified for a given input sequencing file, and thecorresponding fastq files can be obtained from the fastq repositoryserver and loaded into the pipeline.

In a second block, the reads can be filtered. For example, a globalquality filtering of reads in the fastq files can accept reads with aglobal Q-score >30. In one implementation, for each read, theper-position Q-scores are averaged, and if the average is equal orhigher than 30, then the read is accepted, else the read is discarded,as is its paired read.

In a third block, primers can be identified and removed. In oneembodiment, only forward reads that contain the forward primer andreverse reads that contain the reverse primer (allowing annealing ofprimers with up to 5 mismatches or other number of mismatches) arefurther considered. Primers and any sequences 5′ to them are removedfrom the reads. The 125 bp (or other suitable number) towards the 3′ ofthe forward primer are considered from the forward reads, and only 124bp (or other suitable number) towards the 3′ of the reverse primer areconsidered for the reverse reads. All processed forward reads that are<125 bp and reverse reads that are <124 bp are eliminated from furtherprocessing as are their paired reads.

In a fourth block, the forward and reverse reads can be written to files(e.g., FASTA files). For example, the forward and reverse reads thatremained paired can be used to generate files that contain 125 bp fromthe forward read, concatenated to 124 bp from the reverse read (in thereverse complement direction).

In a fifth block, the sequence reads can be clustered, e.g., to identifychimeric sequences or determine a consensus sequence for a bacterium.For example, the sequences in the files can be subjected to clusteringusing the Swarm algorithm with a distance of 1. This treatment allowsthe generation of cluster composed of a central biological entity,surrounded by sequences which are 1 mutation away from the biologicalentity, which are less abundant and the result of the normal basecalling error associated to high throughput sequencing. Singletons areremoved from further analyses. In the remaining clusters, the mostabundant sequence per cluster is then used as the representative andassigned the counts of all members in the cluster.

In a sixth block, chimeric sequences can be removed. For example,amplification of gene superfamilies can produce the formation ofchimeric DNA sequences. These result from a partial PCR product from onemember of the superfamily that anneals and extends over a differentmember of the superfamily in a subsequent cycle of PCR. In order toremove chimeric DNA sequences, some embodiments can use the VSEARCHchimera detection algorithm with the de novo option and standardparameters. This algorithm uses abundance of PCR products to identifyreference “real” sequences as those most abundant, and chimeric productsas those less abundant and displaying local similarity to two or more ofthe reference sequences. All chimeric sequences can be removed fromfurther analysis.

In a seventh block, taxonomy annotation can be assigned to sequencesusing sequence identity searches. To assign taxonomy to the sequencesthat have passed all filters above, some embodiments can performidentity searches against a database that contains bacterial strains(e.g., reference sequences) annotated to phylum, class, order, family,genus and species level, or any other taxonomic levels. The mostspecific level of taxonomy annotation for a sequence can be kept, giventhat higher order taxonomy designations for a lower level taxonomy levelcan be inferred. The sequence identity search can be performed using thealgorithm VSEARCH with parameters (maxaccepts=o, maxrejects=o, id=1)that allow an exhaustive exploration of the reference database used.Decreasing values of sequence identity can be used to assign sequencesto different taxonomic groups: >97% sequence identity for assigning to aspecies, >95% sequence identity for assigning to a genus, >90% forassigning to family, >85% for assigning to order, >80% for assigning toclass, and >77% for assigning to phylum.

In an eighth block, relative abundances of each taxa can be estimatedand output to a database. For example, once all sequences have been usedto identify sequences in the reference database, relative abundance pertaxa can be determined by dividing the count of all sequences that areassigned to the same taxonomic group by the total number of reads thatpassed filters, e.g., were assigned. Results can be uploaded to databasetables that are used as repository for the taxonomic annotation data.

1.2.4 Examples and Variations: Pipeline for Functional Groups

For functional groups, the process can proceed as follows.

In a first step, sample OTUs (Operational Taxonomic Units) can be found.This may occur after the sixth block from above. After the sixth blockabove, sequences can be clustered, e.g., based on sequence identity(e.g., 97% sequence identity).

In a second step, a taxonomy can be assigned, e.g., by comparing OTUswith reference sequences of known taxonomy. The comparison can be basedon sequence identity (e.g., 97%).

In a third step, taxonomic abundance can be adjusted for 16S copynumber, or whatever genomic regions may be analyzed. Different speciesmay have different number of copies of the 16S gene, so those possessinghigher number of copies will have more 16S material for PCRamplification at same number of cells than other species. Therefore,abundance can be normalized by adjusting the number of 16S copies.

In a fourth step, a pre-computed genomic lookup table can be used torelate taxonomy to functions, and amount of function. For example, apre-computed genomic lookup table that shows the number of genes forimportant KEGG or COG functional categories per taxonomic group can beused to estimate the abundance of those functional categories based onthe normalized 16S abundance data.

Upon identification of represented groups of microorganisms of themicrobiome associated with a biological sample and/or identification ofcandidate functional aspects (e.g., functions associated with themicrobiome components of the biological samples), generating featuresderived from compositional and/or functional aspects of the microbiomeassociated with the aggregate set of biological samples can beperformed.

In one variation, generating features can include generating featuresderived from multilocus sequence typing (MLST), which can be performedexperimentally at any stage in relation to implementation of the methods100, 200, in order to identify markers useful for characterization insubsequent blocks of the method 100. Additionally or alternatively,generating features can include generating features that describe thepresence or absence of certain taxonomic groups of microorganisms,and/or ratios between exhibited taxonomic groups of microorganisms.Additionally or alternatively, generating features can includegenerating features describing one or more of: quantities of representedtaxonomic groups, networks of represented taxonomic groups, correlationsin representation of different taxonomic groups, interactions betweendifferent taxonomic groups, products produced by different taxonomicgroups, interactions between products produced by different taxonomicgroups, ratios between dead and alive microorganisms (e.g., fordifferent represented taxonomic groups, based upon analysis of RNAs),phylogenetic distance (e.g., in terms of Kantorovich-Rubinsteindistances, Wasserstein distances etc.), any other suitable taxonomicgroup-related feature(s), any other suitable genetic or functionalfeature(s).

Additionally or alternatively, generating features can includegenerating features describing relative abundance of differentmicroorganism groups, for instance, using a sparCC approach, usingGenome Relative Abundance and Average size (GAAS) approach and/or usinga Genome Relative Abundance using Mixture Model theory (GRAMMy) approachthat uses sequence-similarity data to perform a maximum likelihoodestimation of the relative abundance of one or more groups ofmicroorganisms. Additionally or alternatively, generating features caninclude generating statistical measures of taxonomic variation, asderived from abundance metrics. Additionally or alternatively,generating features can include generating features derived fromrelative abundance factors (e.g., in relation to changes in abundance ofa taxon, which affects abundance of other taxa). Additionally oralternatively, generating features can include generation of qualitativefeatures describing presence of one or more taxonomic groups, inisolation and/or in combination. Additionally or alternatively,generating features can include generation of features related togenetic markers (e.g., representative 16S, 18S, and/or ITS sequences)characterizing microorganisms of the microbiome associated with abiological sample. Additionally or alternatively, generating featurescan include generation of features related to functional associations ofspecific genes and/or organisms having the specific genes. Additionallyor alternatively, generating features can include generation of featuresrelated to pathogenicity of a taxon and/or products attributed to ataxon. Block S120 can, however, include generation of any other suitablefeature(s) derived from sequencing and mapping of nucleic acids of abiological sample. For instance, the feature(s) can be combinatory(e.g., involving pairs, triplets), correlative (e.g., related tocorrelations between different features), and/or related to changes infeatures (i.e., temporal changes, changes across sample sites, etc.,spatial changes, etc.). Features can, however, be generated in any othersuitable manner in Block S120.

1.3 First Method: Supplementary Data

Block S130 recites: receiving a supplementary dataset, associated withat least a subset of the population of subjects, wherein thesupplementary dataset is informative of characteristics associated withthe condition. The supplementary dataset can thus be informative ofpresence of the condition within the population of subjects. Block S130functions to acquire additional data associated with one or moresubjects of the set of subjects, which can be used to train and/orvalidate the characterization processes performed in Block S140. InBlock S130, the supplementary dataset preferably includes survey-deriveddata, but can additionally or alternatively include any one or more of:contextual data derived from sensors, medical data (e.g., current andhistorical medical data associated with a condition associated withmicrobiome functional features), and any other suitable type of data. Invariations of Block S130 including reception of survey-derived data, thesurvey-derived data preferably provides physiological, demographic, andbehavioral information in association with a subject. Physiologicalinformation can include information related to physiological features(e.g., height, weight, body mass index, body fat percent, body hairlevel, etc.). Demographic information can include information related todemographic features (e.g., gender, age, ethnicity, marital status,number of siblings, socioeconomic status, sexual orientation, etc.).Behavioral information can include information related to one or moreof: health conditions (e.g., health and disease states), livingsituations (e.g., living alone, living with pets, living with asignificant other, living with children, etc.), dietary habits (e.g.,omnivorous, vegetarian, vegan, sugar consumption, acid consumption,etc.), behavioral tendencies (e.g., levels of physical activity, druguse, alcohol use, etc.), different levels of mobility (e.g., related todistance traveled within a given time period), different levels ofsexual activity (e.g., related to numbers of partners and sexualorientation), and any other suitable behavioral information.Survey-derived data can include quantitative data and/or qualitativedata that can be converted to quantitative data (e.g., using scales ofseverity, mapping of qualitative responses to quantified scores, etc.).

In facilitating reception of survey-derived data, Block S130 can includeproviding one or more surveys to a subject of the population ofsubjects, or to an entity associated with a subject of the population ofsubjects. Surveys can be provided in person (e.g., in coordination withsample provision and reception from a subject), electronically (e.g.,during account setup by a subject, at an application executing at anelectronic device of a subject, at a web application accessible throughan internet connection, etc.), and/or in any other suitable manner.

Additionally or alternatively, portions of the supplementary datasetreceived in Block S130 can be derived from sensors associated with thesubject(s) (e.g., sensors of wearable computing devices, sensors ofmobile devices, biometric sensors associated with the user, etc.). Assuch, Block S130 can include receiving one or more of: physicalactivity- or physical action-related data (e.g., accelerometer andgyroscope data from a mobile device or wearable electronic device of asubject), environmental data (e.g., temperature data, elevation data,climate data, light parameter data, etc.), patient nutrition ordiet-related data (e.g., data from food establishment check-ins, datafrom spectrophotometric analysis, etc.), biometric data (e.g., datarecorded through sensors within the patient's mobile computing device,data recorded through a wearable or other peripheral device incommunication with the patient's mobile computing device), location data(e.g., using GPS elements), and any other suitable data. Additionally oralternatively, portions of the supplementary dataset can be derived frommedical record data and/or clinical data of the subject(s). As such,portions of the supplementary dataset can be derived from one or moreelectronic health records (EHRs) of the subject(s).

Additionally or alternatively, the supplementary dataset of Block S130can include any other suitable diagnostic information (e.g., clinicaldiagnosis information), which can be combined with analyses derived fromfeatures to support characterization of subjects in subsequent blocks ofthe method 100. For instance, information derived from a colonoscopy,biopsy, blood test, diagnostic imaging, survey-related information, andany other suitable test can be used to supplement Block S130.

1.4 First Method: Characterizations of the Condition Associated withMicrobiome Functional Features

Block S140 recites: transforming the supplementary dataset and featuresextracted from at least one of the microbiome composition dataset andthe microbiome functional diversity dataset into a characterizationmodel of the condition associated with microbiome functional features.Block S140 functions to perform a characterization process foridentifying features and/or feature combinations that can be used tocharacterize subjects or groups with the condition based upon theirmicrobiome composition and/or functional features. Additionally oralternatively, the characterization process can be used as a diagnostictool that can characterize a subject (e.g., in terms of behavioraltraits, in terms of medical conditions, in terms of demographic traits,etc.) based upon their microbiome composition and/or functionalfeatures, in relation to other health condition states, behavioraltraits, medical conditions, demographic traits, and/or any othersuitable traits. Such characterization can then be used to suggest orprovide personalized therapies by way of the therapy model of BlockS150.

In performing the characterization process, Block S140 can usecomputational methods (e.g., statistical methods, machine learningmethods, artificial intelligence methods, bioinformatics methods, etc.)to characterize a subject as exhibiting features characteristic of agroup of subjects with the condition associated with microbiomefunctional features.

In one variation, characterization can be based upon features derivedfrom a statistical analysis (e.g., an analysis of probabilitydistributions) of similarities and/or differences between a first groupof subjects exhibiting a target state (e.g., a health condition state)associated with the condition, and a second group of subjects notexhibiting the target state (e.g., a “normal” state) associated with thecondition. In implementing this variation, one or more of aKolmogorov-Smirnov (KS) test, a permutation test, a Cramér-von Misestest, and any other statistical test (e.g., t-test, Welch's t-test,z-test, chi-squared test, test associated with distributions, etc.) canbe used. In particular, one or more such statistical hypothesis testscan be used to assess a set of features having varying degrees ofabundance in (or variations across) a first group of subjects exhibitinga target state (i.e., an adverse state) associated with the conditionand a second group of subjects not exhibiting the target state (i.e.,having a normal state) associated with the condition. In more detail,the set of features assessed can be constrained based upon percentabundance and/or any other suitable parameter pertaining to diversity inassociation with the first group of subjects and the second group ofsubjects, in order to increase or decrease confidence in thecharacterization. In a specific implementation of this example, afeature can be derived from a taxon of microorganism and/or presence ofa functional feature that is abundant in a certain percentage ofsubjects of the first group and subjects of the second group, wherein arelative abundance of the taxon between the first group of subjects andthe second group of subjects can be determined from one or more of a KStest or a Welch's t-test (e.g., a t-test with a log normaltransformation), with an indication of significance (e.g., in terms ofp-value). Thus, an output of Block S140 can comprise a normalizedrelative abundance value (e.g., 25% greater abundance of a taxon-derivedfeature and/or a functional feature in sick subjects vs. healthysubjects) with an indication of significance (e.g., a p-value of0.0013). Variations of feature generation can additionally oralternatively implement or be derived from functional features ormetadata features (e.g., non-bacterial markers).

In variations and examples, characterization can use the relativeabundance values (RAVs) for populations of subjects that have a disease(condition population) and that do not have the disease (controlpopulation). If the distribution of RAVs of a particular sequence groupfor the condition population is statistically different than thedistribution of RAVs for the control population, then the particularsequence group can be identified for including in a disease signature.Since the two populations have different distributions, the RAV for anew sample for a sequence group in the disease signature can be used toclassify (e.g., determine a probability) of whether the sample does ordoes not have the disease. The classification can also be used todetermine a treatment, as is described herein. A discrimination levelcan be used to identify sequence groups that have a high predictivevalue. Thus, embodiment can filter out taxonomic groups and/orfunctional groups that are not very accurate for providing a diagnosis.

Once RAVs of a sequence group have been determined for the control andcondition populations, various statistical tests can be used todetermine the statistical power of the sequence group for discriminatingbetween disease (condition) and no disease (control). In one embodiment,the Kolmogorov-Smirnov (KS) test can be used to provide a probabilityvalue (p-value) that the two distributions are actually identical. Thesmaller the p-value the greater the probability to correctly identifywhich population a sample belongs. The larger the separation in the meanvalues between the two populations generally results in a smallerp-value (an example of a discrimination level). Other tests forcomparing distributions can be used. The Welch's t-test presumes thatthe distributions are Gaussian, which is not necessarily true for aparticular sequence group. The KS test, as it is a non-parametric test,is well suited for comparing distributions of taxa or functions forwhich the probability distributions are unknown.

The distribution of the RAVs for the control and condition populationscan be analyzed to identify sequence groups with a large separationbetween the two distributions. The separation can be measured as ap-value (See example section). For example, the relative abundancevalues for the control population may have a distribution peaked at afirst value with a certain width and decay for the distribution. And,the condition population can have another distribution that is peaked asecond value that is statistically different than the first value. Insuch an instance, an abundance value of a control sample has a lowerprobability to be within the distribution of abundance valuesencountered for the condition samples. The larger the separation betweenthe two distributions, the more accurate the discrimination is fordetermining whether a given sample belongs to the control population orthe condition population. As is discussed later, the distributions canbe used to determine a probability for an RAV as being in the controlpopulation and determine a probability for the RAV being in thecondition population, where sequence groups associated with the largestpercentage difference between two means have the smallest p-value,signifying a greater separation between the two populations.

In performing the characterization process, Block S140 can additionallyor alternatively transform input data from at least one of themicrobiome composition dataset and microbiome functional diversitydataset into feature vectors that can be tested for efficacy inpredicting characterizations of the population of subjects. Data fromthe supplementary dataset can be used to inform characterizations of thecondition, wherein the characterization process is trained with atraining dataset of candidate features and candidate classifications toidentify features and/or feature combinations that have high degrees (orlow degrees) of predictive power in accurately predicting aclassification. As such, refinement of the characterization process withthe training dataset identifies feature sets (e.g., of subject features,of combinations of features) having high correlation with presence ofthe condition.

In variations, feature vectors effective in predicting classificationsof the characterization process can include features related to one ormore of: microbiome diversity metrics (e.g., in relation to distributionacross taxonomic groups, in relation to distribution across archaeal,bacterial, viral, and/or eukaryotic groups), presence of taxonomicgroups in one's microbiome, representation of specific genetic sequences(e.g., 16S sequences) in one's microbiome, relative abundance oftaxonomic groups in one's microbiome, microbiome resilience metrics(e.g., in response to a perturbation determined from the supplementarydataset), abundance of genes that encode proteins or RNAs with givenfunctions (enzymes, transporters, proteins from the immune system,hormones, interference RNAs, etc.) and any other suitable featuresderived from the microbiome composition dataset, the microbiomefunctional diversity dataset (e.g., COG-derived features, KEGG derivedfeatures, other functional features, etc.), and/or the supplementarydataset. Additionally, combinations of features can be used in a featurevector, wherein features can be grouped and/or weighted in providing acombined feature as part of a feature set. For example, one feature orfeature set can include a weighted composite of the number ofrepresented classes of bacteria in one's microbiome, presence of aspecific genus of bacteria in one's microbiome, representation of aspecific 16S sequence in one's microbiome, and relative abundance of afirst phylum over a second phylum of bacteria. However, the featurevectors can additionally or alternatively be determined in any othersuitable manner.

In examples of Block S140,assuming sequencing has occurred at asufficient depth, one can quantify the number of reads for sequencesindicative of the presence of a feature (e.g., features described inSections 1.4.1-1.4.3 below), thereby allowing one to set a value for anestimated amount of one of the criteria. The number of reads or othermeasures of amount of one of the features can be provided as an absoluteor relative value. An example of an absolute value is the number ofreads of 16S RNA coding sequence reads that map to a specific genus.Alternatively, relative amounts can be determined. An exemplary relativeamount calculation is to determine the amount of 16S RNA coding sequencereads for a particular taxon (e.g., genus , family, order, class, orphylum) relative to the total number of 16S RNA coding sequence readsassigned to the domain. A value indicative of amount of a feature in thesample can then be compared to a cut-off value or a probabilitydistribution in a disease signature for a condition. For example, if thedisease signature indicates that a relative amount of feature #1 of 50%or more of all features possible at that level indicates the likelihoodof a condition, then quantification of gene sequences associated withfeature #1 less than 50% in a sample would indicate a higher likelihoodof healthy (or at least not that specific condition) and alternatively,quantification of gene sequences associated with feature #1 more than50% in a sample would indicate a higher likelihood of disease.

In examples, the taxonomic groups and/or functional groups can bereferred to as features, or as sequence groups in the context ofdetermining an amount of sequence reads corresponding to a particulargroup (feature). In examples, scoring of a particular bacteria orgenetic pathway can be determined according to a comparison of anabundance value to one or more reference (calibration) abundance valuesfor known samples, e.g., where a detected abundance value less than acertain value is associated with the condition in question and above thecertain value is scored as associated with healthy, or vice versadepending on the particular criterion. The scoring for various bacteriaor genetic pathways can be combined to provide a classification for asubject. Furthermore, in the examples, the comparison of an abundancevalue to one or more reference abundance values can include a comparisonto a cutoff value determined from the one or more reference values. Suchcutoff value(s) can be part of a decision tree or a clustering technique(where a cutoff value is used to determine which cluster the abundancevalue(s) belong) that are determined using the reference abundancevalues. The comparison can include intermediate determination of othervalues, (e.g., probability values). The comparison can also include acomparison of an abundance value to a probability distribution of thereference abundance values, and thus a comparison to probability values.

In some embodiments, certain samples may not exhibit any presence of aparticular taxonomic group, or at least not a presence above arelatively low threshold (i.e., a threshold below either of the twodistributions for the control and condition population). Thus, aparticular sequence group may be prevalent in the population, e.g., morethan 30% of the population may have the taxonomic group. Anothersequence group may be less prevalent in the population, e.g., showing upin only 5% of the population. The prevalence (e.g., percentage ofpopulation) of a certain sequence group can provide information as tohow likely the sequence group may be used to determine a diagnosis.

In such an example, the sequence group can be used to determine a statusof the condition (e.g., diagnose for the condition) when the subjectfalls within the 30%. But, when the subject does not fall within the30%, such that the taxonomic group is simply not present, the particulartaxonomic group may not be helpful in determining a diagnosis of thesubject. Thus, whether a particular taxonomic group or functional groupis useful in diagnosing a particular subject can be dependent on whethernucleic acid molecules corresponding to the sequence group are actuallysequenced.

Accordingly, a disease signature can include more sequence groups thatare used for a given subject. As an example, the disease signature caninclude 100 sequence groups, but only 60 of sequence groups may bedetected in a sample. The classification of the subject (including anyprobability for being in the application) would be determined based onthe 60 sequence groups.

In relation to generation of the characterization model, the sequencegroups with high discrimination levels (e.g., low p-values) for a givendisease can be identified and used as part of a characterization model,e.g., which uses a disease signature to determine a probability of asubject having the disease. The disease signature can include a set ofsequence groups as well as discriminating criteria (e.g., cutoff valuesand/or probability distributions) used to provide a classification ofthe subject. The classification can be binary (e.g., disease ornon-disease) or have more classifications (e.g., probability values forhaving the disease or not having the disease). Which sequence groups ofthe disease signature that are used in making a classification bedependent on the specific sequence reads obtained, e.g., a sequencegroup would not be used if no sequence reads were assigned to thatsequence group. In some embodiments, a separate characterization modelcan be determined for different populations, e.g., by geography wherethe subject is currently residing (e.g., country, region, or continent),the generic history of the subject (e.g., ethnicity), or other factors.

1.4.0 Selection of Sequence Groups, Discrimination Criteria SequenceGroups, and Use of Sequence Groups

As mentioned above, sequence groups having at least a specifieddiscrimination level can be selected for inclusion in thecharacterization model. In various embodiments, the specifieddiscrimination level can be an absolute level (e.g., having a p-valuebelow a specified value), a percentage (e.g., being in the top 10% ofdiscriminating levels), or a specified number of the top discriminationlevels (e.g., the top 100 discriminating levels). In some embodiments,the characterization model can include a network graph, where each nodein a graph corresponds to a sequence group having at least a specifieddiscrimination level.

The sequence groups used in a disease signature of a characterizationmodel can also be selected based on other factors. For example, aparticular sequence group may only be detected in a certain percentageof the population, referred to as a coverage percentage. An idealsequence group would be detected in a high percentage of the populationand have a high discriminating level (e.g., a low p-value). A minimumpercentage may be required before adding the sequence group to thecharacterization model for a particular disease. The minimum percentagecan vary based on the accompanying discriminating level. For instance, alower coverage percentage may be tolerated if the discriminating levelis higher. As a further example, 95% of the patients with a conditionmay be classified with one or a combination of a few sequence groups,and the 5% remaining can be explained based on one sequence group, whichrelates to the orthogonality or overlap between the coverage of sequencegroups. Thus, a sequence group that provides discriminating power for 5%of the diseased individuals may be valuable.

Another factor for determining which sequence to include in a diseasesignature of the characterization model is the overlap in the subjectsexhibiting the sequence groups of a disease signature. For example, twosequence groups can both have a high coverage percentage, but sequencegroups may cover the exact same subjects. Thus, adding one of thesequence groups does increase the overall coverage of the diseasesignature. In such a situation, the two sequence groups can beconsidered parallel to each other. Another sequence group can beselected to add to the characterization model based on the sequencegroup covering different subjects than other sequence groups already inthe characterization model. Such a sequence group can be consideredorthogonal to the already existing sequence groups in thecharacterization model.

As examples, selecting a sequence group may consider the followingfactors. A taxa may appear in 100% of healthy individuals and in 100% ofdiseased individuals, but where the distributions are so close in bothgroups, that knowing the relative abundance of that taxa only allows tocatalogue a few individuals as diseased or healthy (i.e. it has a lowdiscriminating level). Whereas, a taxa that appears in only 20% ofhealthy individuals and 30% of diseased individuals can havedistributions of relative abundance that are so different from oneanother, it allows to catalogue 20% of healthy individuals and 30% ofdiseased individuals (i.e. it has a high discriminating level).

In some embodiments, machine learning techniques can allow the automaticidentification of the best combination of features (e.g., sequencegroups). For instance, a Principal Component Analysis can reduce thenumber of features used for classification to only those that are themost orthogonal to each other and can explain most of the variance inthe data. The same is true for a network theory approach, where one cancreate multiple distance metrics based on different features andevaluate which distance metric is the one that best separates diseasedfrom healthy individuals.

The discrimination criteria for the sequence groups included in thedisease signature of a characterization model can be determined based onthe condition distributions and the control distributions for thedisease. For example, a discrimination criterion for a sequence groupcan be a cutoff value that is between the mean values for the twodistributions. As another example, discrimination criteria for asequence group can include probability distributions for the control andcondition populations. The probability distributions can be determinedin a separate manner from the process of determining the discriminationlevel.

The probability distributions can be determined based on thedistribution of RAVs for the two populations. The mean values (or otheraverage or median) for the two populations can be used to center thepeaks of the two probability distributions. For example, if the mean RAVof the condition population is 20% (or 0.2), then the probabilitydistribution for the condition population can have its peak at 20%. Thewidth or other shape parameters (e.g., the decay) can also be determinedbased on the distribution of RAVs for the condition population. The samecan be done for the control population.

The sequence groups included in the disease signature of thecharacterization can be used to classify a new subject. The sequencegroups can be considered features of the feature vector, or the RAVs ofthe sequence groups considered as features of a feature vector, wherethe feature vector can be compared to the discriminating criteria of thedisease signature. For instance, the RAVs of the sequence groups for thenew subject can be compared to the probability distributions for eachsequence group of the disease signature. If an RAV is zero or nearlyzero, then the sequence group may be skipped and not used in theclassification.

The RAVs for sequence groups that are exhibited in the new subject canbe used to determine the classification. For example, the result (e.g.,a probability value) for each exhibited sequence group can be combinedto arrive at the final classification. As another example, clustering ofthe RAVs can be performed, and the clusters can be used to determine aclassification of a condition.

As shown in FIG. 4, in one such alternative variation of Block S140, thecharacterization process can be generated and trained according to arandom forest predictor (RFP) algorithm that combines bagging (i.e.,bootstrap aggregation) and selection of random sets of features from atraining dataset to construct a set of decision trees, T, associatedwith the random sets of features. In using a random forest algorithm, Ncases from the set of decision trees are sampled at random withreplacement to create a subset of decision trees, and for each node, mprediction features are selected from all of the prediction features forassessment. The prediction feature that provides the best split at thenode (e.g., according to an objective function) is used to perform thesplit (e.g., as a bifurcation at the node, as a trifurcation at thenode). By sampling many times from a large dataset, the strength of thecharacterization process, in identifying features that are strong inpredicting classifications can be increased substantially. In thisvariation, measures to prevent bias (e.g., sampling bias) and/or accountfor an amount of bias can be included during processing to increaserobustness of the model.

1.4.1 AIDS Characterization

In one implementation, a characterization process of Block S140 basedupon statistical analyses can identify the sets of features that havethe highest correlations with acquired immune deficiency syndrome(AIDS), for which one or more therapies would have a positive effect,based upon an algorithm trained and validated with a validation datasetderived from a subset of the population of subjects. In particular, AIDSin this first variation is an immune disease characterized byimmunodeficiency, as typically assessed based upon analysis of immuneresponse materials (e.g., cells, antibodies, cytokines, etc.) andcomparison to a given threshold level of a material. In the firstvariation, a set of features useful for diagnostics associated with AIDSincludes features derived from one or more of the following taxa:Prevotellaceae (family), Prevotella (genus), Megasphaera (genus),Veillonellaceae (family), Erysipelotrichaceae (family), Erysipelotrichia(class), Erysipelotrichales (order), Bacteroidia (class), Bacteroidetes(phylum), Bacteroidetes/Chlorobi group (superphylum), Bacteroidales(order), Selenomonadales (order), Negativicutes (class), Lachnospiraceae(family), Flavobacteriia (class), Flavobacteriales (order),Flavobacteriaceae (family), Clostridium (genus), Coprococcus (genus),Porphyromonadaceae (family), Eubacterium ramulus (species),Oscillospiraceae (family), Acidaminococcaceae (family), Lachnospira(genus), Barnesiella (genus), Phascolarctobacterium (genus),Parasutterella (genus), and Parasutterella excrementihominis (species).

Additionally or alternatively, the set of features associated with AIDScan be derived from one or more of: COG derived features, KEGG L2, L3,L4 derived features, and any other suitable functional features. Inspecific examples, such features can include one or more of: aneurodegenerative diseases KEGG L2 derived feature; a transcription KEGGL2 derived feature; a metabolism of cofactors and vitamins KEGG L2derived feature; an endocrine system KEGG L2 derived feature; a cancersKEGG L2 derived feature; an amino acid metabolism KEGG L2 derivedfeature; a glycolysis/gluconeogenesis KEGG L3 derived feature; astreptomycin biosynthesis KEGG L3 derived feature; a restriction enzymeKEGG L3 derived feature; a fatty acid biosynthesis KEGG L3 derivedfeature; a PPAR signaling pathway KEGG L3 derived feature; aphosphotransferase system (PTS) KEGG L3 derived feature; a lipidmetabolism KEGG L3 derived feature; an aminobenzoate degradation KEGG L3derived feature; a pathways in cancer KEGG L3 derived feature; amismatch repair KEGG L3 derived feature; a vitamin B6 metabolism KEGG L3derived feature; a butirosin and neomycin biosynthesis KEGG L3 derivedfeature; a pantothenate and CoA biosynthesis KEGG L3 derived feature; anoxidative phosphorylation KEGG L3 derived feature; a zeatin biosynthesisKEGG L3 derived feature; an energy metabolism KEGG L3 derived feature; alimonene and pinene degradation KEGG L3 derived feature; a valine,leucine, and isoleucine biosynthesis KEGG L3 derived feature; abacterial chemotaxis KEGG L3 derived feature; a homologous recombinationKEGG L3 derived feature; a lipopolysaccharide biosynthesis proteins KEGGL3 derived feature; a transcription machinery KEGG L3 derived feature;and a selenocompound KEGG L3 derived feature.

Thus, characterization of the subject comprises characterization of thesubject as someone with AIDS based upon detection of one or more of theabove features, in a manner that is an alternative or supplemental totypical methods of diagnosis. In variations of the specific example, theset of features can, however, include any other suitable features usefulfor diagnostics.

1.4.2 Gastric Cancer Characterization

In another implementation, a characterization process of Block S140based upon statistical analyses can identify the sets of features thathave the highest correlations with gastric cancer, for which one or moretherapies would have a positive effect, based upon an algorithm trainedand validated with a validation dataset derived from a subset of thepopulation of subjects. In particular, gastric cancer in this firstvariation is a cancerous disease characterized by heartburn, abdominalpain, nausea, vomiting, and blood in the stool, as typically assessedwith one or more of: a gastroscopic exam, an upper gastrointestinalseries (e.g., barium roentgenogram), computed tomography, andhistopathology. In the first variation, a set of features useful fordiagnostics associated with gastric cancer includes features derivedfrom one or more of the following taxa: Streptococcus thermophilus(species).

Additionally or alternatively, the set of features associated withgastric cancer can be derived from one or more of: COG derived features,KEGG L2, L3, L4 derived features, and any other suitable functionalfeatures. In specific examples, such features can include one or moreof: a metabolism of cofactors and vitamins KEGG L2 derived feature; axenobiotics biodegradation and metabolism KEGG L2 derived feature; abenzoate degradation KEGG L3 derived feature; an aminobenzoatedegradation KEGG L3 derived feature; a streptomycin biosynthesis KEGG L3derived feature; and an ABC.CD.P, putative ABC transport system permeaseprotein KEGG L4 derived feature. Thus, characterization of the subjectcomprises characterization of the subject as someone with gastric cancerbased upon detection of one or more of the above features, in a mannerthat is an alternative or supplemental to typical methods of diagnosis.In variations of the specific example, the set of features can, however,include any other suitable features useful for diagnostics.

1.4.3 Sprue/Gluten Intolerance Characterization

In another implementation, a characterization process of Block S140based upon statistical analyses can identify the sets of features thathave the highest correlations with Sprue and/or any other suitable typeof gluten intolerance, for which one or more therapies would have apositive effect, based upon an algorithm trained and validated with avalidation dataset derived from a subset of the population of subjects.In particular, Sprue in this first variation is an autoimmune disorderof the small intestine typically characterized by blood tests fordisease-specific antibodies, and endoscopy. In the first variation, aset of features useful for diagnostics associated with Sprue includesfeatures derived from one or more of the following taxa: Bifidobacterium(genus), Moryella (genus), Dorea (genus), Oscillospira (genus),Intestinimonas (genus), Collinsella (genus), Bacteroides (genus),Dialister (genus), Subdoligranulum (genus), Hespellia (genus), Alistipes(genus), Lachnospira (genus), Faecalibacterium (genus),Bifidobacteriaceae (family), Veillonellaceae (family), Bacteroidaceae(family), Ruminococcaceae (family), Oscillospiraceae (family),Coriobacteriaceae (family), Streptococcaceae (family), Rikenellaceae(family), Prevotellaceae (family), Clostridiales Family XIII. IncertaeSedis (family), Porphyromonadaceae (family), Enterobacteriaceae(family), Bifidobacteriales (order), Selenomonadales (order),Bacteroidales (order), Coriobacteriales (order), Enterobacteriales(order), Clostridiales (order), Actinobacteria (class), Negativicutes(class), Bacteroidia (class), Clostridia (class), Actinobacteria(phylum), Bacteroidetes (phylum), and Firmicutes (phylum).

Additionally or alternatively, the set of features associated with Sprueand/or any other suitable type of gluten intolerance can be derived fromone or more of the following taxa: Anaerostipes (genus), Streptococcusthermophilus (species), Clostridium leptum (species),Chlamydiae/Verrucomicrobia group (superphylum), Verrucomicrobia(phylum), Verrucomicrobiae (class), Verrucomicrobiales (order),Verrucomicrobiaceae (family), Eubacterium siraeum (species),Lachnospiraceae (family), Akkermansia (genus), Negativicutes (class),Selenomonadales (order), Actinobacteria (phylum), Actinobacteridae(subclass), Deltaproteobacteria (class), Ruminococcus faecis (species),Desulfovibrionales (order), and delta/epsilon subdivisions (subphylum).

Additionally or alternatively, the set of features associated with Sprueand/or any other suitable type of gluten intolerance can be derived fromCOG and/or KEGG features including one or more of: a carbohydratemetabolism KEGG L2 derived feature; a metabolism KEGG L2 derivedfeature; a translation KEGG L2 derived feature; a genetic informationprocessing KEGG L2 derived feature; an enzyme families KEGG L2 derivedfeature; a replication and repair KEGG L2 derived feature; a nucleotidemetabolism KEGG L2 derived feature; a transport and catabolism KEGG L2derived feature; a lipid metabolism KEGG L2 derived feature; a signaltransduction KEGG L2 derived feature; a neurodegenerative diseases KEGGL2 derived feature; a metabolism of cofactors and vitamins KEGG L2derived feature; a xenobiotics biodegradation and metabolism KEGG L2derived feature; a folding, sorting, and degradation KEGG L2 derivedfeature; a cell growth and death KEGG L2 derived feature; a biosynthesisof other secondary metabolites KEGG L2 derived feature; an immune systemdiseases KEGG L2 derived feature; an environmental adaptation KEGG L2derived feature; a cellular processes and signaling KEGG L2 derivedfeature; a ribosome biogenesis KEGG L3 derived feature; a peptidoglycanbiosynthesis KEGG L3 derived feature; a biosynthesis and biodegradationof secondary metabolites KEGG L3 derived feature; a translation proteinsKEGG L3 derived feature; a pentose and glucuronate interconversions KEGGL3 derived feature; a chromosome KEGG L3 derived feature; a DNA repairand recombination proteins KEGG L3 derived feature; a Glyoxylate anddicarboxylate metabolism KEGG L3 derived feature; an inositol phosphatemetabolism KEGG L3 derived feature; a ribosome KEGG L3 derived feature;an aminoacyl-tRNA biosynthesis KEGG L3 derived feature; a nicotinate andnicotinamide metabolism KEGG L3 derived feature; an amino acid relatedenzymes KEGG L3 derived feature; a purine metabolism KEGG L3 derivedfeature; a terpenoid backbone biosynthesis KEGG L3 derived feature; atranslation factors KEGG L3 derived feature; a peptidases KEGG L3derived feature; a nucleotide excision repair KEGG L3 derived feature;an RNA polymerase KEGG L3 derived feature; a D-Alanine metabolism KEGGL3 derived feature; a ribosome biogenesis in eukaryotes KEGG L3 derivedfeature; a Cysteine and methionine metabolism KEGG L3 derived feature; aType II diabetes mellitus KEGG L3 derived feature; a homologousrecombination KEGG L3 derived feature; a replication, recombination, andrepair proteins KEGG L3 derived feature; a pentose phosphate pathwayKEGG L3 derived feature; a pyrimidine metabolism KEGG L3 derivedfeature; a carbohydrate metabolism KEGG L3 derived feature; a fructoseand mannose metabolism KEGG L3 derived feature; a pyruvate metabolismKEGG L3 derived feature; a mismatch repair KEGG L3 derived feature; another glycan degradation KEGG L3 derived feature; a one carbon pool byfolate KEGG L3 derived feature; a DNA replication proteins KEGG L3derived feature; a DNA replication KEGG L3 derived feature; a riboflavinmetabolism KEGG L3 derived feature; an other transporters KEGG L3derived feature; a butanoate metabolism KEGG L3 derived feature; asphingolipid metabolism KEGG L3 derived feature; an aminobenzoatedegradation KEGG L3 derived feature; a galactose metabolism KEGG L3derived feature; a cell cycle—Caulobacter KEGG L3 derived feature; aThiamine metabolism KEGG L3 derived feature; a protein export KEGG L3derived feature; a glycerophospholipid metabolism KEGG L3 derivedfeature; a nitrogen metabolism KEGG L3 derived feature; a tuberculosisKEGG L3 derived feature; a two-component system KEGG L3 derived feature;an ethylbenzene degradation KEGG L3 derived feature; a chloroalkane andchloroalkene degradation KEGG L3 derived feature; a pores ion channelsKEGG L3 derived feature; a peroxisome KEGG L3 derived feature; a sulfurmetabolism KEGG L3 derived feature; an inorganic ion transport andmetabolism KEGG L3 derived feature; an amino acid metabolism KEGG L3derived feature; a propanoate metabolism KEGG L3 derived feature; anarginine and proline metabolism KEGG L3 derived feature; a histidinemetabolism KEGG L3 derived feature; a primary immunodeficiency KEGG L3derived feature; a chaperones and folding catalysts KEGG L3 derivedfeature; a base excision repair KEGG L3 derived feature; an amino sugarand nucleotide sugar metabolism KEGG L3 derived feature; aphenylpropanoid biosynthesis KEGG L3 derived feature; a plant-pathogeninteraction KEGG L3 derived feature; an energy metabolism KEGG L3derived feature; a streptomycin biosynthesis KEGG L3 derived feature; aD-glutamine and D-glutamate metabolism KEGG L3 derived feature;, apolycyclic aromatic hydrocarbon degradation KEGG L3 derived feature; alimonene and pinene degradation KEGG L3 derived feature; a lysinedegradation KEGG L3 derived feature; and a zeatin biosynthesis KEGG L3derived feature. Thus, characterization of the subject comprisescharacterization of the subject as someone with Sprue and/or any othersuitable type of gluten intolerance based upon detection of one or moreof the above features, in a manner that is an alternative orsupplemental to typical methods of diagnosis. In variations of thespecific example, the set of features can, however, include any othersuitable features useful for diagnostics.

Characterization of the subject(s) can additionally or alternativelyimplement use of a high false positive test and/or a high false negativetest to further analyze sensitivity of the characterization process insupporting analyses generated according to embodiments of the method100.

In relation to the variations of the first method 100 described above,such variations can additionally or alternatively comprise clusteringotherwise unrelated conditions based upon features extracted from atleast one of a microbiome functional diversity dataset and/or amicrobiome composition dataset by way of a characterization model S140′;and diagnosing a subject with a condition based upon identification ofmicrobiome functional features and/or microbiome composition features,determined upon processing a sample from the subject, in a non-obviousmanner S141′.

In one variation, Blocks S140′ and S141′ can include clusteringseemingly unrelated conditions based upon identification of sharedmicrobiome composition features (e.g., taxonomic features, othercomposition-related features), and generating diagnostics ofcondition(s) based upon extraction and identification of the sharedcomposition features. In a specific example, Blocks S140′ and S141′ caninclude generating diagnostics of one or more of the followingconditions: AIDS, Sprue, and gastric cancer, based upon identificationof a functional features associated with an aminobenzoate degradationKEGG L3 derived feature and/or a streptomycin biosynthesis KEGG L3derived feature in a processed sample, having significant correlationswith presence of the condition. In the specific example, significantcorrelations between the taxonomic feature and conditions can haveassociated p-values of less than 1e-5 and/or 1e-2; however, any othersuitable criterion can be used.

Furthermore, in relation to the conditions described above, diagnosticsbased upon functional features for otherwise unrelated conditions can besupplemented with extraction and/or identification of additionalfeatures related to composition/taxonomic features, as described inSections 1.4.1-1.4.3 above.

Furthermore, in relation to the method(s) described above, a deepsequencing approach can allow for determination of a sufficient numberof copies of DNA sequences to determine relative amount of correspondingbacteria or genetic pathways in the sample. Having identified one ormore of the features described in Sections 1.4.1-1.4.3 above, one cannow diagnose autoimmune conditions in individuals by detecting one ormore of the above features by any quantitative detection method. Forexample, while deep sequencing can be used to detect the presence,absence or amount of one or more option in Sections 1.4.1-1.4.3, one canalso use other detection methods. For example, without intending tolimit the scope of the invention, one could use protein-baseddiagnostics such as immunoassays to detect bacterial taxa by detectingtaxon-specific protein markers.

1.5 First Method: Therapy Models and Provision

As shown in FIG. 1A, in some variations, the first method 100 canfurther include Block S150, which recites: based upon thecharacterization model, generating a therapy model configured to corrector otherwise improve a state of the condition associated with microbiomefunctional features. Block S150 functions to identify or predicttherapies (e.g., probiotic-based therapies, prebiotic-based therapies,phage-based therapies, small molecule-based therapies, etc.) that canshift a subject's microbiome composition and/or functional featurestoward a desired equilibrium state in promotion of the subject's health.In Block S150, the therapies can be selected from therapies includingone or more of: probiotic therapies, phage-based therapies, prebiotictherapies, small molecule-based therapies, cognitive/behavioraltherapies, physical rehabilitation therapies, clinical therapies,medication-based therapies, diet-related therapies, and/or any othersuitable therapy designed to operate in any other suitable manner inpromoting a user's health. In a specific example of abacteriophage-based therapy, one or more populations (e.g., in terms ofcolony forming units) of bacteriophages specific to a certain bacteria(or other microorganism) represented in a subject with condition can beused to down-regulate or otherwise eliminate populations of the certainbacteria. As such, bacteriophage-based therapies can be used to reducethe size(s) of the undesired population(s) of bacteria represented inthe subject. Complementarily, bacteriophage-based therapies can be usedto increase the relative abundances of bacterial populations nottargeted by the bacteriophage(s) used.

For instance, in relation to the variations of conditions in Sections1.4.1 through 1.4.3 above, therapies (e.g., probiotic therapies,bacteriophage-based therapies, prebiotic therapies, etc.) can beconfigured to downregulate and/or upregulate microorganism populationsor subpopulations (and/or functions thereof) associated with featurescharacteristic of the condition.

In one such variation, the Block S150 can include one or more of thefollowing steps: obtaining a sample from the subject; purifying nucleicacids (e.g., DNA) from the sample; deep sequencing nucleic acids fromthe sample so as to determine the amount of one or more of the featuresof one or more of Sections 1.4.1-1.4.3; and comparing the resultingamount of each feature to one or more reference amounts of the one ormore of the features listed in one or more of Sections 1.4.1-1.4.3 asoccurs in an average individual having a condition or an individual nothaving the condition or both. The compilation of features can sometimesbe referred to as a “disease signature” for a specific disease. Thedisease signature can act as a characterization model, and may includeprobability distributions for control population (no disease) orcondition populations having the disease or both. The disease signaturecan include one or more of the features (e.g., bacterial taxa or geneticpathways) in the sections and can optionally include criteria determinedfrom abundance values of the control and/or condition populations.Example criteria can include cutoff or probability values for amounts ofthose features associated with average healthy or diseased individuals.

In a specific example of probiotic therapies, as shown in FIG. 5,candidate therapies of the therapy model can perform one or more of:blocking pathogen entry into an epithelial cell by providing a physicalbarrier (e.g., by way of colonization resistance), inducing formation ofa mucous barrier by stimulation of goblet cells, enhance integrity ofapical tight junctions between epithelial cells of a subject (e.g., bystimulating up regulation of zona-occludens 1, by preventing tightjunction protein redistribution), producing antimicrobial factors,stimulating production of anti-inflammatory cytokines (e.g., bysignaling of dendritic cells and induction of regulatory T-cells),triggering an immune response, and performing any other suitablefunction that adjusts a subject's microbiome away from a state ofdysbiosis.

In variations, the therapy model is preferably based upon data from alarge population of subjects, which can comprise the population ofsubjects from which the microbiome-related datasets are derived in BlockS110, wherein microbiome composition and/or functional features orstates of health, prior exposure to and post exposure to a variety oftherapeutic measures, are well characterized. Such data can be used totrain and validate the therapy provision model, in identifyingtherapeutic measures that provide desired outcomes for subjects basedupon different microbiome characterizations. In variations, supportvector machines, as a supervised machine learning algorithm, can be usedto generate the therapy provision model. However, any other suitablemachine learning algorithm described above can facilitate generation ofthe therapy provision model.

While some methods of statistical analyses and machine learning aredescribed in relation to performance of the Blocks above, variations ofthe method 100 can additionally or alternatively utilize any othersuitable algorithms in performing the characterization process. Invariations, the algorithm(s) can be characterized by a learning styleincluding any one or more of: supervised learning (e.g., using logisticregression, using back propagation neural networks), unsupervisedlearning (e.g., using an Apriori algorithm, using K-means clustering),semi-supervised learning, reinforcement learning (e.g., using aQ-learning algorithm, using temporal difference learning), and any othersuitable learning style. Furthermore, the algorithm(s) can implement anyone or more of: a regression algorithm (e.g., ordinary least squares,logistic regression, stepwise regression, multivariate adaptiveregression splines, locally estimated scatterplot smoothing, etc.), aninstance-based method (e.g., k-nearest neighbor, learning vectorquantization, self-organizing map, etc.), a regularization method (e.g.,ridge regression, least absolute shrinkage and selection operator,elastic net, etc.), a decision tree learning method (e.g.,classification and regression tree, iterative dichotomiser 3, C4.5,chi-squared automatic interaction detection, decision stump, randomforest, multivariate adaptive regression splines, gradient boostingmachines, etc.), a Bayesian method (e.g., naïve Bayes, averagedone-dependence estimators, Bayesian belief network, etc.), a kernelmethod (e.g., a support vector machine, a radial basis function, alinear discriminate analysis, etc.), a clustering method (e.g., k-meansclustering, expectation maximization, etc.), an associated rule learningalgorithm (e.g., an Apriori algorithm, an Eclat algorithm, etc.), anartificial neural network model (e.g., a Perceptron method, aback-propagation method, a Hopfield network method, a self-organizingmap method, a learning vector quantization method, etc.), a deeplearning algorithm (e.g., a restricted Boltzmann machine, a deep beliefnetwork method, a convolution network method, a stacked auto-encodermethod, etc.), a dimensionality reduction method (e.g., principalcomponent analysis, partial lest squares regression, Sammon mapping,multidimensional scaling, projection pursuit, etc.), an ensemble method(e.g., boosting, bootstrapped aggregation, AdaBoost, stackedgeneralization, gradient boosting machine method, random forest method,etc.), and any suitable form of algorithm.

Additionally or alternatively, the therapy model can be derived inrelation to identification of a “normal” or baseline microbiomecomposition and/or functional features, as assessed from subjects of apopulation of subjects who are identified to be in good health. Uponidentification of a subset of subjects of the population of subjects whoare characterized to be in good health (e.g., using features of thecharacterization process), therapies that modulate microbiomecompositions and/or functional features toward those of subjects in goodhealth can be generated in Block S150. Block S150 can thus includeidentification of one or more baseline microbiome compositions and/orfunctional features (e.g., one baseline microbiome for each of a set ofdemographics), and potential therapy formulations and therapy regimensthat can shift microbiomes of subjects who are in a state of dysbiosistoward one of the identified baseline microbiome compositions and/orfunctional features. The therapy model can, however, be generated and/orrefined in any other suitable manner.

Microorganism compositions associated with probiotic therapiesassociated with the therapy model preferably include microorganisms thatare culturable (e.g., able to be expanded to provide a scalable therapy)and non-lethal (e.g., non-lethal in their desired therapeutic dosages).Furthermore, microorganism compositions can comprise a single type ofmicroorganism that has an acute or moderated effect upon a subject'smicrobiome. Additionally or alternatively, microorganism compositionscan comprise balanced combinations of multiple types of microorganismsthat are configured to cooperate with each other in driving a subject'smicrobiome toward a desired state. For instance, a combination ofmultiple types of bacteria in a probiotic therapy can comprise a firstbacteria type that generates products that are used by a second bacteriatype that has a strong effect in positively affecting a subject'smicrobiome. Additionally or alternatively, a combination of multipletypes of bacteria in a probiotic therapy can comprise several bacteriatypes that produce proteins with the same functions that positivelyaffect a subject's microbiome.

In examples of probiotic therapies, probiotic compositions can comprisecomponents of one or more of the identified taxa of microorganisms(e.g., as described in sections 1.4.1 through 1.4.3 above) provided atdosages of 1 million to 10 billion CFUs, as determined from a therapymodel that predicts positive adjustment of a subject's microbiome inresponse to the therapy. Additionally or alternatively, the therapy cancomprise dosages of proteins resulting from functional presence in themicrobiome compositions of subjects without the condition. In theexamples, a subject can be instructed to ingest capsules comprising theprobiotic formulation according to a regimen tailored to one or more ofhis/her: physiology (e.g., body mass index, weight, height),demographics (e.g., gender, age), severity of dysbiosis, sensitivity tomedications, and any other suitable factor.

Furthermore, probiotic compositions of probiotic-based therapies can benaturally or synthetically derived. For instance, in one application, aprobiotic composition can be naturally derived from fecal matter orother biological matter (e.g., of one or more subjects having a baselinemicrobiome composition and/or functional features, as identified usingthe characterization process and the therapy model). Additionally oralternatively, probiotic compositions can be synthetically derived(e.g., derived using a benchtop method) based upon a baseline microbiomecomposition and/or functional features, as identified using thecharacterization process and the therapy model. In variations,microorganism agents that can be used in probiotic therapies can includeone or more of: yeast (e.g., Saccharomyces boulardii), gram-negativebacteria (e.g., E. coli Nissle, Akkermansia muciniphila, Prevotellabryantii, etc.), gram-positive bacteria (e.g., Bifidobacterium animalis(including subspecies lactis), Bifidobacterium longum (includingsubspecies infantis), Bifidobacterium bifidum, Bifidobacteriumpseudolongum, Bifidobacterium thermophilum, Bifidobacterium breve,Lactobacillus rhamnosus, Lactobacillus acidophilus, Lactobacillus casei,Lactobacillus helveticus, Lactobacillus plantarum, Lactobacillusfermentum, Lactobacillus salivarius, Lactobacillus delbrueckii(including subspecies bulgaricus), Lactobacillus johnsonii,Lactobacillus reuteri, Lactobacillus gasseri, Lactobacillus brevis(including subspecies coagulans), Bacillus cereus, Bacillus subtilis(including var. Natto), Bacillus polyfermenticus, Bacillus clausii,Bacillus licheniformis, Bacillus coagulans, Bacillus pumilus,Faecalibacterium prausnitzii, Streptococcus thermophiles, Brevibacillusbrevis, Lactococcus lactis, Leuconostoc mesenteroides, Enterococcusfaecium, Enterococcus faecalis, .Enterococcus durans, Clostridiumbutyricum, Sporolactobacillus inulinus, Sporolactobacillus vineae,Pediococcus acidilactic, Pediococcus pentosaceus, etc.), and any othersuitable type of microorganism agent.

Additionally or alternatively, therapies promoted by the therapy modelof Block S150 can include one or more of: consumables (e.g., food items,beverage items, nutritional supplements), suggested activities (e.g.,exercise regimens, adjustments to alcohol consumption, adjustments tocigarette usage, adjustments to drug usage), topical therapies (e.g.,lotions, ointments, antiseptics, etc.), adjustments to hygienic productusage (e.g., use of shampoo products, use of conditioner products, useof soaps, use of makeup products, etc.), adjustments to diet (e.g.,sugar consumption, fat consumption, salt consumption, acid consumption,etc.), adjustments to sleep behavior, living arrangement adjustments(e.g., adjustments to living with pets, adjustments to living withplants in one's home environment, adjustments to light and temperaturein one's home environment, etc.), nutritional supplements (e.g.,vitamins, minerals, fiber, fatty acids, amino acids, prebiotics,probiotics, etc.), medications, antibiotics, and any other suitabletherapeutic measure. Among the prebiotics suitable for treatment, aseither part of any food or as supplement, are included the followingcomponents: 1,4-dihydroxy-2-naphthoic acid (DHNA), Inulin,trans-Galactooligosaccharides (GOS), Lactulose, Mannan oligosaccharides(MOS), Fructooligosaccharides (FOS), Neoagaro-oligosaccharides (NAOS),Pyrodextrins, Xylo-oligosaccharides (XOS), Isomalto-oligosaccharides(IMOS), Amylose-resistant starch, Soybean oligosaccharide (SBOS),Lactitol, Lactosucrose (LS), Isomaltulose (including Palatinose),Arabinoxylooligosaccharides (AXOS), Raffinose oligosaccharides (RFO),Arabinoxylans (AX), Polyphenols or any another compound capable ofchanging the microbiota composition with a desirable effect.

Additionally or alternatively, therapies promoted by the therapy modelof Block S150 can include one or more of: surgical methods (e.g., inrelation to cancers, in relation to gastrointestinal conditions, inrelation to endocrine system conditions); radiation therapy;medications; nutritional supplements; dietary guidance; stimulants;therapeutic interventions to support, replace, or improve function of aportion of the endocrine system glands; steroid-based treatments;immunosuppressants; cognitive behavioral therapy (CBT)-based techniques;exercise therapy; and any other suitable treatment or therapy configuredto improve a state of a condition associated with the microbiomefunctional features.

The first method 100 can, however, include any other suitable blocks orsteps configured to facilitate reception of biological samples fromindividuals, processing of biological samples from individuals,analyzing data derived from biological samples, and generating modelsthat can be used to provide customized diagnostics and/or therapeuticsaccording to specific microbiome compositions of individuals.

1.6 Example Method

Embodiments can provide a method for determining a classification of thepresence or absence for a condition and/or determine a course oftreatment for an individual human having the condition. The method canbe performed by a computer system.

In step 1, sequence reads of bacterial DNA obtained from analyzing atest sample from the individual human are received. The analysis can bedone with various techniques, e.g., as described herein, such assequencing or hybridization arrays. The sequence reads can be receivedat a computer system, e.g., from a detection apparatus, such as asequencing machine that provides data to a storage device (which can beloaded into the computer system) or across a network to the computersystem.

In step 2, the sequence reads are mapped to a bacterial sequencedatabase to obtain a plurality of mapped sequence reads. The bacterialsequence database includes a plurality of reference sequences of aplurality of bacteria. The reference sequences can be for predeterminedregion(s) of the bacteria, e.g., the 16S region.

In step 3, the mapped sequence reads are assigned to sequence groupsbased on the mapping to obtain assigned sequence reads assigned to atleast one sequence group. A sequence group includes one or more of theplurality of reference sequences. The mapping can involve the sequencereads being mapped to one or more predetermined regions of the referencesequences. For example, the sequence reads can be mapped to the 16Sgene. Thus, the sequence reads do not have to be mapped to the wholegenome, but only to the region(s) covered by the reference sequences ofa sequence group.

In step 4, a total number of assigned sequence reads is determined. Insome embodiments, the total number of assigned reads can include readsidentified as being bacterial, but not assigned to a known sequencegroup. In other embodiments, the total number can be a sum of sequencereads assigned to known sequence groups, where the sum may include anysequence read assigned to at least one sequence group.

In step 5, relative abundance value(s) can be determined. For example,for each sequence group of a disease signature set of one or moresequence groups associated with features described in Sections1.4.1-1.4.3 above, a relative abundance value of assigned sequence readsassigned to the sequence group relative to the total number of assignedsequence reads can be determined. The relative abundance values can forma test feature vector, where each value of the test feature vector is anRAV of a different sequence group.

In step 6, the test feature vector is compared to calibration featurevectors generated from relative abundance values of calibration sampleshaving a known status of the condition. The calibration samples may besamples of a condition population and samples of a control population.In some embodiments, the comparison can involve various machine learningtechniques, such as supervised machine learning (e.g. decision trees,nearest neighbor, support vector machines, neural networks, naïve Bayesclassifier, etc.) and unsupervised machine learning (e.g., clustering,principal component analysis, etc.).

In one embodiment, clustering can use a network approach, where thedistance between each pair of samples in the network is computed basedon the relative abundance of the sequence groups that are relevant foreach condition. Then, a new sample can be compared to all samples in thenetwork, using the same metric based on relative abundance, and it canbe decided to which cluster it should belong. A meaningful distancemetric would allow all diseased individuals to form one or a fewclusters and all healthy individuals to form one or a few clusters. Onedistance metric is the Bray-Curtis dissimilarity, or equivalently asimilarity network, where the metric is 1—Bray-Curtis dissimilarity.Another example distance metric is the Tanimoto coefficient.

In some embodiments, the feature vectors may be compared by transformingthe RAVs into probability values, thereby forming probability vectors.Similar processing for the feature vectors can be performed for theprobability, with such a process still involving a comparison of thefeature vectors since the probability vectors are generated from thefeature vectors.

Step 7 can determine a classification of the presence or absence of thecondition and/or determine a course of treatment for an individual humanhaving condition based on the comparing. For example, the cluster towhich the test feature vector is assigned may be a condition cluster,and the classification can be made that the individual human has thecondition or a certain probability for having the condition.

In one embodiment involving clustering, the calibration feature vectorscan be clustered into a control cluster not having the condition and acondition cluster having the condition. Then, which cluster the testfeature vector belongs can be determined. The identified cluster can beused to determine the classification or select a course of treatment. Inone implementation, the clustering can use a Bray-Curtis dissimilarity.

In one embodiment involving a decision tree, the comparison may beperformed to by comparing the test feature vector to one or more cutoffvalues (e.g., as a corresponding cutoff vector), where the one or morecutoff values are determined from the calibration feature vectors,thereby providing the comparison. Thus, the comparing can includecomparing each of the relative abundance values of the test featurevector to a respective cutoff value determined from the calibrationfeature vectors generated from the calibration samples. The respectivecutoff values can be determined to provide an optimal discrimination foreach sequence group.

A new sample can be measured to detect the RAVs for the sequence groupsin the disease signature. The RAV for each sequence group can becompared to the probability distributions for the control and conditionspopulations for the particular sequence group. For example, theprobability distribution for the condition population can provide anoutput of a probability (condition probability) of having the conditionfor a given input of the RAV. Similarly, the probability distributionfor the control population can provide an output of a probability(control probability) of not having the condition for a given input ofthe RAV. Thus, the value of the probability distribution at the RAV canprovide the probability of the sample being in each of the populations.Thus, it can be determined which population the sample is more likely tobelong to, by taking the maximum probability.

A total probability across sequence groups of a disease signature can beused. For all of the sequence groups that are measured, a conditionprobability can be determined for whether the sample is in the conditiongroup and a control probability can be determined for whether the sampleis in the control population. In other embodiments, just the conditionprobabilities or just the control probabilities can be determined.

The probabilities across the sequence groups can be used to determine atotal probability. For example, an average of the conditionprobabilities can be determined, thereby obtaining a final conditionprobability of the subject having the condition based on the diseasesignature. An average of the control probabilities can be determined,thereby obtaining a final control probability of the subject not havingthe condition based on the disease signature.

In one embodiment, the final condition probability and final controlprobability can be compared to each other to determine the finalclassification. For instance, a difference between the two finalprobabilities can be determined, and a final classification probabilitydetermined from the difference. A large positive difference with finalcondition probability being higher would result in a higher finalclassification probability of the subject having the disease.

In other embodiments, only the final condition probability can be usedto determine the final classification probability. For example, thefinal classification probability can be the final condition probability.Alternatively, the final classification probability can be one minus thefinal control probability, or 100% minus the final control probabilitydepending on the formatting of the probabilities.

In some embodiments, a final classification probability for one diseaseof a class can be combined with other final classification probabilitiesof other diseases of the same class. The aggregated probability can thenbe used to determine whether the subject has at least one of the classesof diseases. Thus, embodiments can determine whether a subject has ahealth issue that may include a plurality of diseases associated withthat health issue.

The classification can be one of the final probabilities. In otherexamples, embodiments can compare a final probability to a thresholdvalue to make a determination of whether the condition exists. Forexample, the respective condition probabilities can be averaged, and anaverage can be compared to a threshold value to determine whether thecondition exists. As another example, the comparison of the average tothe threshold value can provide a treatment for treating the subject.

2. Method for Generating Microbiome-Derived Diagnostics

In some embodiments, as noted above, outputs of the first method 100 canbe used to generate diagnostics and/or provide therapeutic measures foran individual based upon an analysis of the individual's microbiome. Assuch, a second method 200 derived from at least one output of the firstmethod 100 can include: receiving a biological sample from a subjectS210; characterizing the subject with a form of a condition associatedwith the microbiome functional features based upon processing amicrobiome dataset derived from the biological sample S220; andpromoting a therapy to the subject with condition based upon thecharacterization and the therapy model S230.

Block S210 recites: receiving a biological sample from the subject,which functions to facilitate generation of a microbiome compositiondataset and/or a microbiome functional diversity dataset for thesubject. As such, processing and analyzing the biological samplepreferably facilitates generation of a microbiome composition datasetand/or a microbiome functional diversity dataset for the subject, whichcan be used to provide inputs that can be used to characterize theindividual in relation to diagnosis of the condition, as in Block S220.Receiving a biological sample from the subject is preferably performedin a manner similar to that of one of the embodiments, variations,and/or examples of sample reception described in relation to Block S110above. As such, reception and processing of the biological sample inBlock S210 can be performed for the subject using similar processes asthose for receiving and processing biological samples used to generatethe characterization(s) and/or the therapy provision model of the firstmethod 100, in order to provide consistency of process. However,biological sample reception and processing in Block S210 canalternatively be performed in any other suitable manner.

Block S220 recites: characterizing the subject characterizing thesubject with a form of condition based upon processing a microbiomedataset derived from the biological sample. Block S220 functions toextract features from microbiome-derived data of the subject, and usethe features to positively or negatively characterize the individual ashaving a form of the condition. Characterizing the subject in Block S220thus preferably includes identifying features and/or combinations offeatures associated with the microbiome composition and/or functionalfeatures of the microbiome of the subject, and comparing such featureswith features characteristic of subjects with the condition. Block S220can further include generation of and/or output of a confidence metricassociated with the characterization for the individual. For instance, aconfidence metric can be derived from the number of features used togenerate the classification, relative weights or rankings of featuresused to generate the characterization, measures of bias in the modelsused in Block S140 above, and/or any other suitable parameter associatedwith aspects of the characterization operation of Block S140.

In some variations, features extracted from the microbiome dataset canbe supplemented with survey-derived and/or medical history-derivedfeatures from the individual, which can be used to further refine thecharacterization operation(s) of Block S220. However, the microbiomecomposition dataset and/or the microbiome functional diversity datasetof the individual can additionally or alternatively be used in any othersuitable manner to enhance the first method 100 and/or the second method200.

Block S230 recites: promoting a therapy to the subject with thecondition based upon the characterization and the therapy model. BlockS230 functions to recommend or provide a personalized therapeuticmeasure to the subject, in order to shift the microbiome composition ofthe individual toward a desired equilibrium state. As such, Block S230can include correcting condition, or otherwise positively affecting theuser's health in relation to the condition. Block S230 can thus includepromoting one or more therapeutic measures to the subject based upontheir characterization in relation to the condition, as described inrelation to Sections 1.4.1 through 1.4.3 above, wherein the therapy isconfigured to modulate taxonomic makeup of the subject's microbiomeand/or modulate functional feature aspects of the subject in a desiredmanner toward a “normal” state in relation to the characterizationsdescribed above.

In Block S230, providing the therapeutic measure to the subject caninclude recommendation of available therapeutic measures configured tomodulate microbiome composition of the subject toward a desired state.Additionally or alternatively, Block S230 can include provision ofcustomized therapy to the subject according to their characterization(e.g., in relation to a specific type of condition). In variations,therapeutic measures for adjusting a microbiome composition of thesubject, in order to improve a state of the condition can include one ormore of: probiotics, prebiotics, bacteriophage-based therapies,consumables, suggested activities, topical therapies, adjustments tohygienic product usage, adjustments to diet, adjustments to sleepbehavior, living arrangement, adjustments to level of sexual activity,nutritional supplements, medications, antibiotics, and any othersuitable therapeutic measure. Therapy provision in Block S230 caninclude provision of notifications by way of an electronic device,through an entity associated with the individual, and/or in any othersuitable manner.

In more detail, therapy provision in Block S230 can include provision ofnotifications to the subject regarding recommended therapeutic measuresand/or other courses of action, in relation to health-related goals, asshown in FIG. 6. Notifications can be provided to an individual by wayof an electronic device (e.g., personal computer, mobile device, tablet,head-mounted wearable computing device, wrist-mounted wearable computingdevice, etc.) that executes an application, web interface, and/ormessaging client configured for notification provision. In one example,a web interface of a personal computer or laptop associated with asubject can provide access, by the subject, to a user account of thesubject, wherein the user account includes information regarding thesubject's characterization, detailed characterization of aspects of thesubject's microbiome composition and/or functional features, andnotifications regarding suggested therapeutic measures generated inBlock S150. In another example, an application executing at a personalelectronic device (e.g., smart phone, smart watch, head-mounted smartdevice) can be configured to provide notifications (e.g., at a display,haptically, in an auditory manner, etc.) regarding therapeuticsuggestions generated by the therapy model of Block S150. Notificationscan additionally or alternatively be provided directly through an entityassociated with a subject (e.g., a caretaker, a spouse, a significantother, a healthcare professional, etc.). In some further variations,notifications can additionally or alternatively be provided to an entity(e.g., healthcare professional) associated with the subject, wherein theentity is able to administer the therapeutic measure (e.g., by way ofprescription, by way of conducting a therapeutic session, etc.).Notifications can, however, be provided for therapy administration tothe subject in any other suitable manner.

Furthermore, in an extension of Block S230, monitoring of the subjectduring the course of a therapeutic regimen (e.g., by receiving andanalyzing biological samples from the subject throughout therapy, byreceiving survey-derived data from the subject throughout therapy) canbe used to generate a therapy-effectiveness model for each recommendedtherapeutic measure provided according to the model generated in BlockS150.

The methods 100, 200 and/or system of the embodiments can be embodiedand/or implemented at least in part as a machine configured to receive acomputer-readable medium storing computer-readable instructions. Theinstructions can be executed by computer-executable componentsintegrated with the application, applet, host, server, network, website,communication service, communication interface,hardware/firmware/software elements of a patient computer or mobiledevice, or any suitable combination thereof. Other systems and methodsof the embodiments can be embodied and/or implemented at least in partas a machine configured to receive a computer-readable medium storingcomputer-readable instructions. The instructions can be executed bycomputer-executable components integrated with apparatuses and networksof the type described above. The computer-readable medium can be storedon any suitable computer readable media such as RAMs, ROMs, flashmemory, EEPROMs, optical devices (CD or DVD), hard drives, floppydrives, or any suitable device. The computer-executable component can bea processor, though any suitable dedicated hardware device can(alternatively or additionally) execute the instructions.

The FIGURES illustrate the architecture, functionality and operation ofpossible implementations of systems, methods and computer programproducts according to preferred embodiments, example configurations, andvariations thereof. In this regard, each block in the flowchart or blockdiagrams may represent a module, segment, step, or portion of code,which comprises one or more executable instructions for implementing thespecified logical function(s). It should also be noted that, in somealternative implementations, the functions noted in the block can occurout of the order noted in the FIGURES. For example, two blocks shown insuccession may, in fact, be executed substantially concurrently, or theblocks may sometimes be executed in the reverse order, depending uponthe functionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts, or combinations of special purpose hardware andcomputer instructions.

As a person skilled in the art will recognize from the previous detaileddescription and from the figures and claims, modifications and changescan be made to the embodiments of the invention without departing fromthe scope of this invention as defined in the following claims.

1. A method for characterizing a gastric cancer condition, the methodcomprising: receiving an aggregate set of samples from a population ofsubjects; for the aggregate set of samples: determining microorganismnucleic acid sequences, comprising: identifying a set of primers fornucleic acid sequences associated with the gastric cancer condition,amplifying nucleic acid material from the aggregate set of samplesamples based on the set of primers; and determining alignments of themicroorganism nucleic acid sequences to reference sequences associatedwith the gastric cancer condition, including aminobenzoate-relatedcondition; generating a microbiome feature dataset for the population ofsubjects based upon the alignments; generating a characterization modelof the gastric cancer condition from the microbiome feature dataset;based upon the characterization model, generating a therapy model thatdetermines a therapy for correcting the gastric cancer condition; and atan output device associated with the subject, providing the therapy to asubject diagnosed with the gastric cancer condition based upon thecharacterization model and the therapy model.
 2. The method of claim 1,wherein amplifying the nucleic acid material comprises amplifying withat least one of: a) bridge amplification substrates of a next generationsequencing platform and b) moiety-bound particles.
 3. The method ofclaim 1, wherein determining microorganism nucleic acid sequencescomprises identifying the set of primers for the nucleic acid sequenceassociated with the gastric cancer condition based upon a primerselection model.
 4. The method of claim 1, wherein generating thecharacterization model further comprises performing at least one of: agastroscopic examination, a histopathological test, and a stool sampleevaluation for at least one of the population of subjects.
 5. The methodof claim 1, wherein determining the microorganism nucleic acid sequencecomprises performing, at a library preparation subsystem, multiplexamplification of nucleic acid material of the sample using the set ofprimers.
 6. The method of claim 1, wherein generating thecharacterization model comprises analyzing a set of features from themicrobiome feature dataset with a statistical analysis, wherein the setof features includes features associated with one or more of: presenceof microbiome features, absence of microbiome features, a relativeabundance of different taxonomic groups represented in the microbiomecomposition dataset, a ratio between at least two features comprisingtaxonomic groups and functional features, interactions between differenttaxonomic groups represented in the microbiome composition dataset, andphylogenetic distance between taxonomic groups represented in themicrobiome composition dataset.
 7. The method of claim 6, whereingenerating the characterization comprises analyzing the set of featuresextracted from the microbiome feature dataset with a statisticalanalysis comprising prediction analysis associated with at least one of:multi hypothesis testing, random forest testing, and principal componentanalysis.
 8. The method of claim 1, wherein generating thecharacterization model of the gastric cancer condition comprisesevaluating features of the microbiome feature dataset associated with aset of functional features comprising at least one of: a first featureassociated with metabolism of cofactors and vitamins and a secondfeature associated with xenobiotics biodegradation and metabolism. 9.The method of claim 8, wherein the set of functional features furthercomprises at least one of: a third feature associated with benzoatedegradation and a fourth feature associated with aminobenzoatedegradation.
 10. The method of claim 8, wherein the set of functionalfeatures further comprises at least one of: a fifth feature associatedwith streptomycin biosynthesis and a sixth feature associated withputative ABC transport system permease protein.
 11. The method of claim1, wherein generating the characterization model of the gastric cancercondition comprises evaluating features of the microbiome featuredataset associated with a set of taxonomic features associated withStreptococcus thermophilus (species).
 12. A method for diagnosing andtreating a gastric cancer condition of a subject, the method comprising:receiving a sample from the subject; determining nucleic acid sequencesof a microorganism component of the sample upon amplifying nucleic acidmaterial of the sample with a primer; generating a microbiome featuredataset for the subject based upon the nucleic acid sequences of themicroorganism component; generating a characterization of the gastriccancer condition in the subject upon processing the microbiome featuredataset with a characterization model derived from a population ofsubjects; and at an output device associated with the subject, providinga therapy to the subject with the gastric cancer condition uponprocessing the characterization with a therapy model.
 13. The method ofclaim 12, wherein determining nucleic acid sequences further comprisesidentifying the primer for the nucleic acid sequence with a primerselection model.
 14. The method of claim 12, wherein generating thecharacterization comprises evaluating features of the microbiome featuredataset associated with Streptococcus thermophilus (species).
 15. Themethod of claim 14, wherein generating the characterization comprisesevaluating features of the microbiome feature dataset associated with afirst feature associated with metabolism of cofactors and vitamins and asecond feature associated with xenobiotics biodegradation andmetabolism.
 16. The method of claim 15, wherein generating thecharacterization comprises evaluating features of the microbiome featuredataset associated with a third feature associated with benzoatedegradation and a fourth feature associated with aminobenzoatedegradation.
 17. The method of claim 16, wherein generating thecharacterization comprises evaluating features of the microbiome featuredataset associated with a fifth feature associated with streptomycinbiosynthesis and a sixth feature associated with putative ABC transportsystem permease protein.
 18. The method of claim 12, wherein providingthe therapy comprises providing a consumable to the subject, theconsumable affecting a microorganism component that selectively supportsmodulation of microbiome function in the subject, associated withcorrection of the gastric cancer condition, based on the therapy model.19. The method of claim 13, wherein providing the therapy furthercomprises at least one of following medical procedures: a surgicaloperation, medications, and radiation therapy.
 20. The method of claim12, wherein providing the therapy comprises establishing an account in apatient portal for the subject, and providing information pertaining tothe gastric condition to the subject, through the patient portal.